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Donovan, S.

Publications and source records attributed to Donovan, S..

6 recordsLinked to original sources

Calbindin Stratifies Midbrain Dopaminergic Neurons Governing Distinct Aspects of Locomotion

Despite advances in delineating the molecular diversity and projection patterns of midbrain dopaminergic (DA) neurons, their specific contributions to locomotion and motor learning remain poorly defined. Here, we applied intersectional ablation and chemogenetic approaches to dissect the distinct roles of calbindin-expressing (CALB1+) and non-expressing (CALB1-) DA neurons in locomotion. Using newly engineered intersectional constructs, we ablated CALB1+ or CALB1- DA neurons in the mouse midbrain. Loss of either subtype led to pronounced deficits in the initiation and vigor of voluntary movements, as demonstrated by a reduction in peak speed, acceleration and deceleration of locomotor bouts. Notably, only CALB1- ablation disrupted locomotor learning. Beyond these functional effects, we observed that selective ablation of CALB1 DA neurons induced local microglial activation and was followed by a non-cell-autonomous loss of CALB1- DA neurons, suggesting that CALB1- neurons are more vulnerable to inflammation triggered by CALB1 neuron loss. We then confirmed these findings by performing acute inhibition of either population using inhibitory DREADD hM4Di. CALB1- DA neurons inhibition impaired the initial acquisition of locomotor learning, whereas inhibition of CALB1+ DA neurons disrupted the retention of acquired motor skills from previous days. Moreover, inhibition of CALB1+ DA neurons further impaired the initiation and amplitude of voluntary movements, as well as the velocity and acceleration/deceleration of locomotor bouts. Together, these findings provide causal evidence for functional specialization among molecularly distinct midbrain DA subtypes and reveal new aspects of mesostriatal circuit organization underlying locomotion and motor memory.

neuroscience↗

Biofoundry-scale DNA assembly validation using cost-effective high-throughput long read sequencing

Biofoundries are automated high-throughput facilities specialising in the design, construction and testing of engineered/synthetic DNA constructs (plasmids), often from genetic parts. A critical step of this process is assessing the fidelity of the assembled DNA construct to the desired design. Current methods utilised for this purpose are restriction digest or PCR followed by fragment analysis, and sequencing. The Edinburgh Genome Foundry (EGF) has recently established a single-molecule sequencing quality control step using the Oxford Nanopore sequencing technology, along with a companion Nextflow pipeline and a Python package to perform in-depth analysis and generate a detailed report. Our software enables biofoundry scientists and end-users to rapidly analyse sequencing data, without specialised bioinformatics knowledge. In conclusion, we have created a laboratory and software protocol that validates assembled, cloned or edited plasmids, using Nanopore long reads, which can serve as a useful resource for the genetics, synthetic biology and sequencing communities. Author informationAll authors contributed to the design of the sequencing quality control step and pipeline, and the preparation of the manuscript. P.V. wrote the manuscript, designed and implemented the bioinformatics pipeline and interpreted results. S.D. wrote the manuscript, implemented the laboratory protocol and interpreted results. G.S. designed the bioinformatics pipeline. R.F. wrote the manuscript and contributed to the design of the laboratory protocol and pipeline. Address: Edinburgh Genome Foundry (University of Edinburgh), Michael Swann Building, Max Born Crescent, Edinburgh, EH9 3BF, United Kingdom Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/558498v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@a819e5org.highwire.dtl.DTLVardef@3c34ceorg.highwire.dtl.DTLVardef@aef5d8org.highwire.dtl.DTLVardef@8e5d2f_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗

Cost-efficient whole genome-sequencing using novel mostly natural sequencing-by-synthesis chemistry and open fluidics platform

We introduce a massively parallel novel sequencing platform that combines an open flow cell design on a circular wafer with a large surface area and mostly natural nucleotides that allow optical end-point detection without reversible terminators. This platform enables sequencing billions of reads with longer read length ([~]300bp) and fast runs times (<20hrs) with high base accuracy (Q30 > 85%), at a low cost of $1/Gb. We establish system performance by whole-genome sequencing of the Genome-In-A-Bottle reference samples HG001-7, demonstrating high accuracy for SNPs (99.6%) and Indels in homopolymers up to length 10 (96.4%) across the vast majority (>98%) of the defined high-confidence regions of these samples. We demonstrate scalability of the whole-genome sequencing workflow by sequencing an additional 224 selected samples from the 1000 Genomes project achieving high concordance with reference data.

genomics↗

Decoupling diet from microbiome dynamics results in model mis-specification that implicitly annuls potential associations between the microbiome and disease phenotypes-ruling out any role of the microbiome in autism (Yap et al. 2021) likely a premature conclusion

Recently, and in a tour de force effort, Yap et al. performed a comprehensive association analysis of factors such as demographics, psychometrics, diet, stool metagenomics, stool consistency, and genome-wide SNP genotypes with autism in 247 Australian children. Surprisingly, the authors suggest their data show a strong correlation between diet and autism spectrum disorder (ASD) but only negligible, if any, ASD-specific microbiome signals. While the first conclusion comes as no surprise, we were rather puzzled by the second conclusion given the growing evidence of strong associations between the microbiome and ASD phenotype and the wide consensus on a close connection between diet and microbiome composition and function. The causal model proposed by Yap et al. seemed to imply that diet and the microbiome were two independent variables. A careful review of the approach used by the authors confirmed our suspicions that the statistical models were mis-specified, i.e. they had a questionable biological assumption--the independence of diet and microbiome--embedded in them. We have run side-by-side simulations of the causal linear model proposed by Yap et al. and of an analogous model in which diet and the microbiome are treated as co-dependent variables. We show how the Yap et al. model can preemptively exclude any potential host-microbe interactions if the diet-microbiome independence assumption is violated. We believe large-scale efforts such as the one described by Yap et al. are essential to advance our understanding of the potential role of the microbiome in ASD and other diseases. But these are highly complex systems to analyze and thus ensuring that the statistical methods used are accurate is essential to avoid drawing any potentially misleading conclusions due to subtle causal assumptions propagated by the statistical models themselves.

microbiology↗

Multi-omic analysis along the gut-brain axis points to a functional architecture of autism

Autism is a highly heritable neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut-brain axis (GBA) has been implicated in autism, with dozens of cross-sectional microbiome and other omic studies revealing autism-specific profiles along the GBA albeit with little agreement in composition or magnitude. To explore the functional architecture of autism, we developed an age and sex-matched Bayesian differential ranking algorithm that identified autism-specific profiles across 10 cross-sectional microbiome datasets and 15 other omic datasets, including dietary patterns, metabolomics, cytokine profiles, and human brain expression profiles. The analysis uncovered a highly significant, functional architecture along the GBA that encapsulated the overall heterogeneity of autism phenotypes. This architecture was determined by autism-specific amino acid, carbohydrate and lipid metabolism profiles predominantly encoded by microbial species in the genera Prevotella, Enterococcus, Bifidobacterium, and Desulfovibrio, and was mirrored in brain-associated gene expression profiles and restrictive dietary patterns in individuals with autism. Pro-inflammatory cytokine profiling and virome association analysis further supported the existence of an autism-specific architecture associated with particular microbial genera. Re-analysis of a longitudinal intervention study in autism recapitulated the cross-sectional profiles, and showed a strong association between temporal changes in microbiome composition and autism symptoms. Further elucidation of the functional architecture of autism, including of the role the microbiome plays in it, will require deep, multi-omic longitudinal intervention studies on well-defined stratified cohorts to support causal and mechanistic inference.

microbiology↗

Barriers to Integration of Bioinformatics into Undergraduate Life Sciences Education

Bioinformatics, a discipline that combines aspects of biology, statistics, and computer science, is increasingly important for biological research. However, bioinformatics instruction is rarely integrated into life sciences curricula at the undergraduate level. To understand why, the Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE, \"nibbles\") recently undertook an extensive survey of life sciences faculty in the United States. The survey responses to open-ended questions about barriers to integration were subjected to keyword analysis. The barrier most frequently reported by the ~1,260 respondents was lack of faculty training. Faculty at associates-granting institutions report the least training in bioinformatics and the least integration of bioinformatics into their teaching. Faculty from underrepresented minority groups (URMs) in STEM reported training barriers at a higher rate than others, although the number of URM respondents was small. Interestingly, the cohort of faculty with the most recently awarded PhD degrees reported the most training but were teaching bioinformatics at a lower rate than faculty who earned their degrees in previous decades. Other barriers reported included lack of student interest in bioinformatics; lack of student preparation in mathematics, statistics, and computer science; already overly full curricula; and limited access to resources, including hardware, software, and vetted teaching materials. The results of the survey, the largest to date on bioinformatics education, will guide efforts to further integrate bioinformatics instruction into undergraduate life sciences education.

scientific communication and education↗