ABCC9 Is Downregulated and Prone to Microsatellite Instability on ABCC9tetra in Canine Breast Cancer

Tumorigenesis is associated with metabolic abnormalities and genomic instability. Microsatellite mutations, including microsatellite instability (MSI) and loss of heterozygosity (LOH), are associated with the functional impairment of some tumor-related genes. To investigate the role of MSI and LOH in sporadic breast tumors in canines, 22 tumors DNA samples and their adjacent normal tissues were evaluated using polyacrylamide gel electrophoresis and silver staining for 58 microsatellites. Quantitative real-time polymerase chain reaction, promoter methylation analysis and immunohistochemical staining were used to quantify gene expression. The results revealed that a total of 14 tumors (6 benign tumors and 8 breast cancers) exhibited instability as MSI-Low tumors. Most of the microsatellite loci possessed a single occurrence of mutations. The maximum number of MSI mutations on loci was observed in tumors with a lower degree of differentiation. Among the unstable markers, FH2060 (4/22), ABCC9tetra (4/22) and SCN11A (6/22) were high-frequency mutation sites, whereas FH2060 was a high-frequency LOH site (4/22). The ABCC9tetra locus was mutated only in cancerous tissue, although it was excluded by transcription. The corresponding genes and proteins were significantly downregulated in malignant tissues, particularly in tumors with MSI. Furthermore, the promoter methylation results of the adenosine triphosphate binding cassette subfamily C member 9 (ABCC9) showed that there was a high level of methylation in breast tissues, but only one case showed a significant elevation compared with the control. In conclusion, MSI-Low or MSI-Stable is characteristic of most sporadic mammary tumors. Genes associated with tumorigenesis are more likely to develop MSI. ABCC9 protein and transcription abnormalities may be associated with ABCC9tetra instability.

Autogenous and anautogenous Culex pipiens bioforms exhibit insulin-like peptide signaling pathway gene expression differences that are not dependent upon larval nutrition

Culex pipiens form pipiens and Cx. pipiens form molestus differ in their ability to produce eggs without a bloodmeal. Autogenous mosquitoes, such as the molestus bioform of Cx. pipiens, depend on nutrition acquired as larvae instead of a bloodmeal to fuel the energy intensive process of vitellogenesis, which requires abundant production of yolk proteins. In anautogenous mosquito systems, ovary ecdysteroidogenic hormone (OEH) and insulin-like peptides (ILPs) transduce nutritional signals and trigger egg maturation in response to a bloodmeal. It is unclear to what extent the process is conserved in autogenous mosquitoes and how the bloodmeal trigger has been replaced by teneral reserves. Here, we measured the effects of a series of nutritional regimens on autogeny, time to pupation, and survival in Cx. pipiens form molestus and form pipiens. We find that abundant nutrients never result in autogenous form pipiens and extremely poor food availability rarely eliminates autogeny from form molestus. However, the number of autogenous eggs generated increases with nutrient availability. Similarly, using qPCR to quantify gene expression, we find several differences in the expression levels of ilps between bioforms that are reduced and delayed by poor nutrition, but not extinguished. Changes in OEH expression do not explain bioform-specific differences in autogeny. Surprisingly, the source of most of the gene expression differences correlated with autogeny is the abdomen, not the brain. Overall, our results suggest that autogeny is modulated by nutritional availability, but the trait is encoded by genetic differences between forms and these impact the expression of ILPs.

Museum of Spatial Transcriptomics

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors depends on the spatial organization of their cells. In the past decade high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To assess the ability and potential of spatial gene expression technologies to drive biological discovery, we present a curated database of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field such as usage of experimental techniques, species, tissues studied and computational approaches used. Our analysis places current methods in historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/.

Zea mays RNA-seq estimated transcript abundances are strongly affected by read mapping bias

Background Genetic variation for gene expression is a source of phenotypic variation for natural and agricultural species. The common approach to map and to quantify gene expression from genetically distinct individuals is to assign their RNA-seq reads to a single reference genome. However, RNA-seq reads from alleles dissimilar to this reference genome may fail to map correctly, causing transcript levels to be underestimated. Presently, the extent of this mapping problem is not clear, particularly in highly diverse species. We investigated if mapping bias occurred and if chromosomal features associated with mapping bias. Zea mays presents a model species to assess these questions, given it has genotypically distinct and well-studied genetic lines. Results In Zea mays, the inbred B73 genome is the standard reference genome and template for RNA-seq read assignments. In the absence of mapping bias, B73 and a second inbred line, Mo17, would each have an approximately equal number of regulatory alleles that increase gene expression. Remarkably, Mo17 had 2–4 times fewer such positively acting alleles than did B73 when RNA-seq reads were aligned to the B73 reference genome. Reciprocally, over one-half of the B73 alleles that increased gene expression were not detected when reads were aligned to the Mo17 genome template. Genes at dissimilar chromosomal ends were strongly affected by mapping bias, and genes at more similar pericentromeric regions were less affected. Biased transcript estimates were higher in untranslated regions and lower in splice junctions. Bias occurred across software and alignment parameters. Conclusions Mapping bias very strongly affects gene transcript abundance estimates in maize, and bias varies across chromosomal features. Individual genome or transcriptome templates are likely necessary for accurate transcript estimation across genetically variable individuals in maize and other species.

Heat shock protein gene expression is higher and more variable with thermal stress and mutation accumulation in Daphnia

Understanding the genetic architecture of the stress response and its ability to evolve in response to different stressors requires an integrative approach. Here we quantify gene expression changes in response to two stressors associated with global climate change and habitat loss—heat shock and mutation accumulation. We measure expression levels for two Heat Shock Proteins (HSP90 and HSP60)—members of an important family of conserved molecular chaperones that have been shown to play numerous roles in the cell. While HSP90 assists with protein folding, stabilization, and degradation throughout the cell, HSP60 primarily localizes to the mitochondria and mediates de novo folding and stress-induced refolding of proteins. We perform these assays in Daphnia magna originally collected from multiple genotypes and populations along a latitudinal gradient, which differ in their annual mean, maximum, and range of temperatures. We find significant differences in overall expression between loci (10-fold), in response to thermal stress (~6x increase) and with mutation accumulation (~4x increase). Importantly, stressors interact synergistically to increase gene expression levels when more than one is applied (increasing, on average, >20x). While there is no evidence for differences among the three populations assayed, individual genotypes vary considerably in HSP90 expression. Overall, our results support previous proposals that HSP90 may act as an important buffer against not only heat, but also mutation, and expands this hypothesis to include another member of the gene family acting in a different domain of the cell.

Optimized design of antisense oligomers for targeted rRNA depletion

RNA sequencing (RNA-seq) is extensively used to quantify gene expression transcriptome-wide. Although often paired with polyadenylate (poly(A)) selection to enrich for messenger RNA (mRNA), many applications require alternate approaches to counteract the high proportion of ribosomal RNA (rRNA) in total RNA. Recently, digestion using RNaseH and antisense DNA oligomers tiling target rRNAs has emerged as an alternative to commercial rRNA depletion kits. Here, we present a streamlined, more economical RNaseH-mediated rRNA depletion with substantially lower up-front costs, using shorter antisense oligos only sparsely tiled along the target RNA in a 5-min digestion reaction. We introduce a novel Web tool, Oligo-ASST, that simplifies oligo design to target regions with optimal thermodynamic properties, and additionally can generate compact, common oligo pools that simultaneously target divergent RNAs, e.g. across different species. We demonstrate the efficacy of these strategies by generating rRNA-depletion oligos for Xenopus laevis and for zebrafish, which expresses two distinct versions of rRNAs during embryogenesis. The resulting RNA-seq libraries reduce rRNA to <5% of aligned reads, on par with poly(A) selection, and also reveal expression of many non-adenylated RNA species. Oligo-ASST is freely available at https://mtleelab.pitt.edu/oligo to design antisense oligos for any taxon or to target any abundant RNA for depletion.

CrustyBase: an interactive online database for crustacean transcriptomes

Transcriptome sequencing has opened the field of genomics to a wide variety of researchers, owing to its efficiency, applicability across species and ability to quantify gene expression. The resulting datasets are a rich source of information that can be mined for many years into the future, with each dataset providing a unique angle on a specific context in biology. Maintaining accessibility to this accumulation of data presents quite a challenge for researchers. The primary focus of conventional genomics databases is the storage, navigation and interpretation of sequence data, which is typically classified down to the level of a species or individual. The addition of expression data adds a new dimension to this paradigm – the sampling context. Does gene expression describe different tissues, a temporal distribution or an experimental treatment? These data not only describe an individual, but the biological context surrounding that individual. The structure and utility of a transcriptome database must therefore reflect these attributes. We present an online database which has been designed to maximise the accessibility of crustacean transcriptome data by providing intuitive navigation within and between datasets and instant visualization of gene expression and protein structure. The site is accessible at https://crustybase.org and currently holds 10 datasets from a range of crustacean species. It also allows for upload of novel transcriptome datasets through a simple web interface, allowing the research community to contribute their own data to a pool of shared knowledge.

Novel frameshift variant (c.409dupG) in SLC25A38 is a common cause of congenital sideroblastic anaemia in the Indian subcontinent

AimsCongenital sideroblastic anaemias (CSAs) are a group of rare disorders with the presence of ring sideroblasts in the bone marrow. Pathogenic variants are inherited in an autosomal recessive/X-linked fashion. The study was aimed at characterising the spectrum of mutations in SLC25A38 and ALAS2 genes in sideroblastic anaemia patients, exploring the genotype-phenotype correlation and identifying the haplotype associated with any recurrent mutation.Patients and methodsTwenty probable CSA patients were retrospectively analysed for genetic variants in ALAS2 and SLC25A38 genes by direct bidirectional sequencing. Real-time PCR was used to quantify gene expression in a case with promoter region variant in ALAS2. Three single nucleotide polymorphisms were used to establish the haplotype associated with a recurrent variant in the SLC25A38 gene.ResultsSix patients had causative variants in ALAS2 (30%) and 11 had variants in SLC25A38 (55%). The ALAS2 mutated cases presented at a significantly later age than the SLC25A38 cases. A frameshift variant in SLC25A38 (c.409dupG) was identified in six unrelated patients and was a common variant in our population exhibiting ‘founder effect’.ConclusionThis is the largest series of sideroblastic anaemia cases with molecular characterisation from the Indian subcontinent.

Bayesian inference of the gene expression states of single cells from scRNA-seq data

In spite of a large investment in the development of methodologies for analysis of single-cell RNA-seq data, there is still little agreement on how to best normalize such data, i.e. how to quantify gene expression states of single cells from such data. Starting from a few basic requirements such as that inferred expression states should correct for both intrinsic biological fluctuations and measurement noise, and that changes in expression state should be measured in terms of fold-changes rather than changes in absolute levels, we here derive a unique Bayesian procedure for normalizing single-cell RNA-seq data from first principles. Our implementation of this normalization procedure, called Sanity (SAmpling Noise corrected Inference of Transcription activitY), estimates log expression values and associated errors bars directly from raw UMI counts without any tunable parameters.Comparison of Sanity with other recent normalization methods on a selection of scRNA-seq datasets shows that Sanity outperforms other methods on basic downstream processing tasks such as clustering cells into subtypes and identification of differentially expressed genes. More importantly, we show that all other normalization methods present severely distorted pictures of the data. By failing to account for biological and technical Poisson noise, many methods systematically predict the lowest expressed genes to be most variable in expression, whereas in reality these genes provide least evidence of true biological variability. In addition, by confounding noise removal with lower-dimensional representation of the data, many methods introduce strong spurious correlations of expression levels with the total UMI count of each cell as well as spurious co-expression of genes.

Selection and evaluation of reference genes for analysis of mouse(Mus musculus) sex-dimorphic brain development

The development of the brain is sex-dimorphic, and as a result so are many neurological disorders. One approach for studying sex-dimorphic brain development is to measure gene expression in biological samples using RT-qPCR. However, the accuracy and consistency of this technique relies on the reference gene(s) selected. We analyzed the expression of ten reference genes in male and female samples over three stages of brain development, using popular algorithms NormFinder, GeNorm and Bestkeeper. The top ranked reference genes at each time point were further used to quantify gene expression of three sex-dimorphic genes (Wnt10b,XistandCYP7B1). When comparing gene expression between the sexes expression at specific time points the best reference gene combinations are:Sdha/Pgk1at E11.5,RpL38/SdhaE12.5, andActb/RpL37at E15.5. When studying expression across time, the ideal reference gene(s) differs with sex. For XY samples a combination ofActb/Sdha. In contrast, when studying gene expression across developmental stage with XX samples,Sdha/Gapdhwere the top reference genes. Our results identify the best combination of two reference genes when studying male and female brain development, and emphasize the importance of selecting the correct reference genes for comparisons between developmental stages.