Uncovering co-regulatory modules and gene regulatory networks in the heart through machine learning-based analysis of large-scale epigenomic data.

The availability of large-scale epigenomic data from various cell types and conditions has yielded valuable insights for evaluating and learning features predicting the co-binding of transcription factors (TF). However, prior attempts to develop models predicting motif co-occurrence lacked scalability for globally analyzing any motif combination or making cross-species predictions. Moreover, mapping co-regulatory modules (CRM) to gene regulatory networks (GRN) is crucial for understanding underlying function. Currently, no comprehensive pipeline exists for large-scale, rapid, and accurate CRM and GRN identification. In this study, we analyzed and evaluated different TF binding characteristics facilitating biologically significant co-binding to identify all potential clusters of co-binding TFs. We curated the UniBind database, containing ChIP-Seq data from over 1983 samples and 232 TFs, and implemented two machine learning models to predict CRMs and the potential regulatory networks they operate on. Two machine learning models, Convolution Neural Networks (CNN) and Random Forest Classifier(RFC), used to predict co-binding between TFs, were compared using precision-recall Receiver Operating Characteristic (ROC) curves. CNN outperformed RFC (AUC 0.94 vs. 0.88) and achieved higher F1 scores (0.938 vs. 0.872). The CRMs generated by the clustering algorithm were validated against ChipAtlas and MCOT, revealing additional motifs forming CRMs. We predicted 200k CRMs for 50k+ human genes, validated against recent CRM prediction methods with 100% overlap. Further, we narrowed our focus to study heart-related regulatory motifs, filtering the generated CRMs to report 1784 Cardiac CRMs containing at least four cardiac TFs. Identified cardiac CRMs revealed potential novel regulators like ARID3A and RXRB for SCAD, including known TFs like PPARG for F11R. Our findings highlight the importance of the NKX family of transcription factors in cardiac development and provide potential targets for further investigation in cardiac disease.

EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes.

The adoption of electronic health records (EHRs) has created opportunities to analyse historical data for predicting clinical outcomes and improving patient care. However, non-standardised data representations and anomalies pose major challenges to the use of EHRs in digital health research. To address these challenges, we have developed EHR-QC, a tool comprising two modules: the data standardisation module and the preprocessing module. The data standardisation module migrates source EHR data to a standard format using advanced concept mapping techniques, surpassing expert curation in benchmarking analysis. The preprocessing module includes several functions designed specifically to handle healthcare data subtleties. We provide automated detection of data anomalies and solutions to handle those anomalies. We believe that the development and adoption of tools like EHR-QC is critical for advancing digital health. Our ultimate goal is to accelerate clinical research by enabling rapid experimentation with data-driven observational research to generate robust, generalisable biomedical knowledge.

Linc2function: A Comprehensive Pipeline and Webserver for Long Non-Coding RNA (lncRNA) Identification and Functional Predictions Using Deep Learning Approaches.

Long non-coding RNAs (lncRNAs), comprising a significant portion of the human transcriptome, serve as vital regulators of cellular processes and potential disease biomarkers. However, the function of most lncRNAs remains unknown, and furthermore, existing approaches have focused on gene-level investigation. Our work emphasizes the importance of transcript-level annotation to uncover the roles of specific transcript isoforms. We propose that understanding the mechanisms of lncRNA in pathological processes requires solving their structural motifs and interactomes. A complete lncRNA annotation first involves discriminating them from their coding counterparts and then predicting their functional motifs and target bio-molecules. Current in silico methods mainly perform primary-sequence-based discrimination using a reference model, limiting their comprehensiveness and generalizability. We demonstrate that integrating secondary structure and interactome information, in addition to using transcript sequence, enables a comprehensive functional annotation. Annotating lncRNA for newly sequenced species is challenging due to inconsistencies in functional annotations, specialized computational techniques, limited accessibility to source code, and the shortcomings of reference-based methods for cross-species predictions. To address these challenges, we developed a pipeline for identifying and annotating transcript sequences at the isoform level. We demonstrate the effectiveness of the pipeline by comprehensively annotating the lncRNA associated with two specific disease groups. The source code of our pipeline is available under the MIT licensefor local use by researchers to make new predictions using the pre-trained models or to re-train models on new sequence datasets. Non-technical users can access the pipeline through a web server setup.

Biosynthetic constraints on amino acid synthesis at the base of the food chain may determine their use in higher-order consumer genomes.

Dietary nutrient composition is essential for shaping important fitness traits and behaviours. Many organisms are protein limited, and for Drosophila melanogaster this limitation manifests at the level of the single most limiting essential Amino Acid (AA) in the diet. The identity of this AA and its effects on female fecundity is readily predictable by a procedure called exome matching in which the sum of AAs encoded by a consumer's exome is used to predict the relative proportion of AAs required in its diet. However, the exome matching calculation does not weight AA contributions to the overall profile by protein size or expression. Here, we update the exome matching calculation to include these weightings. Surprisingly, although nearly half of the transcriptome is differentially expressed when comparing male and female flies, we found that creating transcriptome-weighted exome matched diets for each sex did not enhance their fecundity over that supported by exome matching alone. These data indicate that while organisms may require different amounts of dietary protein across conditions, the relative proportion of the constituent AAs remains constant. Interestingly, we also found that exome matched AA profiles are generally conserved across taxa and that the composition of these profiles might be explained by energetic and elemental limitations on microbial AA synthesis. Thus, it appears that ecological constraints amongst autotrophs shape the relative proportion of AAs that are available across trophic levels and that this constrains biomass composition.

Preterm labor with and without chorioamnionitis is associated with activation of myometrial inflammatory networks: a comprehensive transcriptomic analysis.

BACKGROUND: The onset of preterm labor is associated with inflammation. Previous studies suggested that this is distinct from the inflammation observed during term labor. Our previous work on 44 genes differentially expressed in myometria in term labor demonstrated a different pattern of gene expression from that observed in preterm laboring and nonlaboring myometria. We found increased expression of inflammatory genes in preterm labor associated with chorioamnionitis, but in the absence of chorioamnionitis observed no difference in gene expression in preterm myometria regardless of laboring status, suggesting that preterm labor is associated with different myometrial genes or signals originating from outside the myometrium. Given that a small subset of genes were assessed, this study aimed to use RNA sequencing and bioinformatics to assess the myometrial transcriptome during preterm labor in the presence and absence of chorioamnionitis. OBJECTIVE: This study aimed to comprehensively determine protein-coding transcriptomic differences between preterm nonlaboring and preterm laboring myometria with and without chorioamnionitis. STUDY DESIGN: Myometria were collected at cesarean delivery from preterm patients not in labor (n=16) and preterm patients in labor with chorioamnionitis (n=8) or without chorioamnionitis (n=6). Extracted RNA from myometrial tissue was prepared and sequenced using Illumina NovaSeq. Gene expression was quantified by mapping the sequence reads to the human reference genome (hg38). Differential gene expression analysis, gene set enrichment analysis, and weighted gene coexpression network analysis were used to comprehensively interrogate transcriptomic differences and their associated biology. RESULTS: Differential gene expression analysis comparing preterm patients in labor with chorioamnionitis with preterm patients not in labor identified 931 differentially expressed genes, whereas comparing preterm patients in labor without chorioamnionitis with preterm patients not in labor identified no statistically significant gene expression changes. In contrast, gene set enrichment analysis and weighted gene coexpression network analysis demonstrated that preterm labor with and without chorioamnionitis was associated with enrichment of pathways involved in activation of the innate immune system and inflammation, and activation of G protein-coupled receptors. Key genes identified included chemotactic CYP4F3, CXCL8, DOCK2, and IRF1 in preterm labor with chorioamnionitis and CYP4F3, FCAR, CHUK, and IL13RA2 in preterm labor without chorioamnionitis. There was marked overlap in the pathways enriched in both preterm labor subtypes. CONCLUSION: Differential gene expression analysis demonstrated that myometria from preterm patients in labor without chorioamnionitis and preterm patients not in labor were transcriptionally similar, whereas the presence of chorioamnionitis was associated with marked gene changes. In contrast, comprehensive bioinformatic analysis indicated that preterm labor with or without chorioamnionitis was associated with innate immune activation. All causes of preterm labor were associated with activation of the innate immune system, but this was more marked in the presence of chorioamnionitis. These data suggest that anti-inflammatory therapy may be relevant in managing preterm labor of all etiologies.

Transcriptomic analysis reveals myometrial topologically associated domains linked to the onset of human term labour.

Changes in cell phenotype are thought to occur through the expression of groups of co-regulated genes within topologically associated domains (TADs). In this paper, we allocate genes expressed within the myometrium of the human uterus during the onset of term labour into TADs. Transformation of the myometrial cells of the uterus into a contractile phenotype during term human labour is the result of a complex interaction of different epigenomic and genomic layers. Recent work suggests that the transcription factor (TF) RELA lies at the top of this regulatory network. Using deep RNA sequencing (RNAseq) analysis of myometrial samples (n = 16) obtained at term from women undergoing caesarean section prior to or after the onset of labour, we have identified evidence for how other gene expression regulatory elements interact with TFs in the labour phenotype transition. Gene set enrichment analysis of our RNAseq data identified three modules of enriched genes (M1, M2 and M3), which in gene ontology studies are linked to matrix degradation, smooth muscle and immune gene signatures, respectively. These genes were predominantly located within chromosomal TADs suggesting co-regulation of expression. Our transcriptomic analysis also identified significant differences in the expression of long non-coding RNAs (lncRNA), microRNAs (miRNA) and TFs that were predicted to target genes within the TADs. Additionally, network analysis revealed 15 new lncRNA (MCM3AP-AS1, TUG1, MIR29B2CHG, HCG18, LINC00963, KCNQ1OT1, NEAT1, HELLPAR, SNHG16, NUTM2B-AS1, MALAT1, PSMA3-AS1, GABPB1-AS1, NORAD and NKILA) and 4 miRNA (mir-145, mir-223, mir-let-7a and mir-132) as top gene hubs with three TFs (NFKB1, RELA and ESR1) as master regulators. Together, these factors are likely to be involved in co-regulatory networks driving a myometrial transformation to generate an estrogen-sensitive phenotype. We conclude that lncRNA and miRNA targeting the estrogen receptor 1 and nuclear factor kappa B pathways play a key role in the initiation of human labour. For the first time, we perform an integrative analysis to present a multi-level genomic signature made of mRNA, non-coding RNA and TFs in the myometrium for spontaneous term labour.

A Survey of Current Resources to Study lncRNA-Protein Interactions.

Phenotypes are driven by regulated gene expression, which in turn are mediated by complex interactions between diverse biological molecules. Protein-DNA interactions such as histone and transcription factor binding are well studied, along with RNA-RNA interactions in short RNA silencing of genes. In contrast, lncRNA-protein interaction (LPI) mechanisms are comparatively unknown, likely directed by the difficulties in studying LPI. However, LPI are emerging as key interactions in epigenetic mechanisms, playing a role in development and disease. Their importance is further highlighted by their conservation across kingdoms. Hence, interest in LPI research is increasing. We therefore review the current state of the art in lncRNA-protein interactions. We specifically surveyed recent computational methods and databases which researchers can exploit for LPI investigation. We discovered that algorithm development is heavily reliant on a few generic databases containing curated LPI information. Additionally, these databases house information at gene-level as opposed to transcript-level annotations. We show that early methods predict LPI using molecular docking, have limited scope and are slow, creating a data processing bottleneck. Recently, machine learning has become the strategy of choice in LPI prediction, likely due to the rapid growth in machine learning infrastructure and expertise. While many of these methods have notable limitations, machine learning is expected to be the basis of modern LPI prediction algorithms.

A multi-modal data harmonisation approach for discovery of COVID-19 drug targets.

Despite the volume of experiments performed and data available, the complex biology of coronavirus SARS-COV-2 is not yet fully understood. Existing molecular profiling studies have focused on analysing functional omics data of a single type, which captures changes in a small subset of the molecular perturbations caused by the virus. As the logical next step, results from multiple such omics analysis may be aggregated to comprehensively interpret the molecular mechanisms of SARS-CoV-2. An alternative approach is to integrate data simultaneously in a parallel fashion to highlight the inter-relationships of disease-driving biomolecules, in contrast to comparing processed information from each omics level separately. We demonstrate that valuable information may be masked by using the former fragmented views in analysis, and biomarkers resulting from such an approach cannot provide a systematic understanding of the disease aetiology. Hence, we present a generic, reproducible and flexible open-access data harmonisation framework that can be scaled out to future multi-omics analysis to study a phenotype in a holistic manner. The pipeline source code, detailed documentation and automated version as a R package are accessible. To demonstrate the effectiveness of our pipeline, we applied it to a drug screening task. We integrated multi-omics data to find the lowest level of statistical associations between data features in two case studies. Strongly correlated features within each of these two datasets were used for drug-target analysis, resulting in a list of 84 drug-target candidates. Further computational docking and toxicity analyses revealed seven high-confidence targets, amsacrine, bosutinib, ceritinib, crizotinib, nintedanib and sunitinib as potential starting points for drug therapy and development.

Transcriptome-wide changes associated with the reproductive behaviour of male guppies exposed to 17α-ethinyl estradiol.

Although many pharmaceutical compounds (and their metabolites) can induce harmful impacts at the molecular, physiological and behavioural levels, their underlying mechanistic associations have remained largely unexplored. Here, we utilized RNA-Seq to build a whole brain transcriptome profile to examine the impact of a common endocrine disrupting pharmaceutical (17α-ethinyl estradiol, EE2) on reproductive behaviour in wild guppies (Poecilia reticulata). Specifically, we annotated 16,791 coding transcripts in whole brain tissue in relation to the courtship behaviour (i.e. sigmoid display) of EE2 exposed (at environmentally relevant concentration of 8 ng/L for 28-days) and unexposed guppies. Further, we obtained 10,960 assembled transcripts matching in the non-coding orthologous genomes. Behavioural responses were assessed using a standard mate choice experiment, which allowed us to disentangle chemical cues from visual cues. We found that a high proportion of the RNAseq reads aligned back to our de novo assembled transcriptome with 80.59% mapping rate. Behavioural experiments showed that when males were presented only with female visual cues, there was a significant interaction between male treatment and female treatment in the time spent in the preference zone. This is one of the first studies to show that transcriptome-wide changes are associated with the reproductive behaviour of fish: EE2 exposed male guppies that performed high levels of courtship had a gene profile that deviated the most from the other treatment groups, while both non-courting EE2 and control males had similar gene signatures. Using Gene Ontology pathway analysis, our study shows that EE2-exposed males had gene transcripts enriched for pathways associated with altered immunity, starvation, altered metabolism and spermatogenesis. Our study demonstrates that multiple gene networks orchestrate courting behaviour, emphasizing the importance of investigating impacts of pharmaceuticals on gene networks instead of single genes.

Towards a comprehensive pipeline to identify and functionally annotate long noncoding RNA (lncRNA).

Long noncoding RNAs (lncRNAs) are implicated in various genetic diseases and cancer, attributed to their critical role in gene regulation. They are a divergent group of RNAs and are easily differentiated from other types with unique characteristics, functions, and mechanisms of action. In this review, we provide a list of some of the prominent data repositories containing lncRNAs, their interactome, and predicted and validated disease associations. Next, we discuss various wet-lab experiments formulated to obtain the data for these repositories. We also provide a critical review of in silico methods available for the identification purpose and suggest techniques to further improve their performance. The bulk of the methods currently focus on distinguishing lncRNA transcripts from the coding ones. Functional annotation of these transcripts still remains a grey area and more efforts are needed in that space. Finally, we provide details of current progress, discuss impediments, and illustrate a roadmap for developing a generalized computational pipeline for comprehensive annotation of lncRNAs, which is essential to accelerate research in this area.

Integrative computational epigenomics to build data-driven gene regulation hypotheses.

BACKGROUND: Diseases are complex phenotypes often arising as an emergent property of a non-linear network of genetic and epigenetic interactions. To translate this resulting state into a causal relationship with a subset of regulatory features, many experiments deploy an array of laboratory assays from multiple modalities. Often, each of these resulting datasets is large, heterogeneous, and noisy. Thus, it is non-trivial to unify these complex datasets into an interpretable phenotype. Although recent methods address this problem with varying degrees of success, they are constrained by their scopes or limitations. Therefore, an important gap in the field is the lack of a universal data harmonizer with the capability to arbitrarily integrate multi-modal datasets. RESULTS: In this review, we perform a critical analysis of methods with the explicit aim of harmonizing data, as opposed to case-specific integration. This revealed that matrix factorization, latent variable analysis, and deep learning are potent strategies. Finally, we describe the properties of an ideal universal data harmonization framework. CONCLUSIONS: A sufficiently advanced universal harmonizer has major medical implications, such as (i) identifying dysregulated biological pathways responsible for a disease is a powerful diagnostic tool; (2) investigating these pathways further allows the biological community to better understand a disease's mechanisms; and (3) precision medicine also benefits from developments in this area, particularly in the context of the growing field of selective epigenome editing, which can suppress or induce a desired phenotype.

CEP3 levels affect starvation-related growth responses of the primary root.

CEPs (C-TERMINALLY ENCODED PEPTIDEs) inhibit Arabidopsis primary root growth by unknown mechanisms. We investigated how CEP3 levels control primary root growth. CEP3 peptide application decreased cell division, S-phase cell number, root meristematic cell number, and meristem zone (MZ) size in a dose- and CEP RECEPTOR1-dependent manner. Grafting showed that CEP3-dependent growth inhibition requires root and shoot CEPR1. CEP3 induced mitotic quiescence in MZ cells significantly faster than that induced by nutrient limitation alone. CEP3 also inhibited the restoration of S-phase to mitotically quiescence cells by nutrient resupply without quantitatively reducing TARGET OF RAPAMYCIN (TOR) kinase activity. In contrast, cep3-1 had an increased meristem size and S-phase cell number under nitrogen (N)-limited conditions, but not under N-sufficient conditions. Furthermore, cep3-1 meristematic cells remained in S-phase longer than wild-type cells during a sustained carbon (C) and N limitation. RNA sequencing showed that CEP3 peptide down-regulated genes involved in S-phase entry, cell wall and ribosome biogenesis, DNA replication, and meristem expansion, and up-regulated genes involved in catabolic processes and proteins and peptides that negatively control meristem expansion and root growth. Many of these genes were reciprocally regulated in cep3-1. The results suggest that raising CEP3 induces starvation-related responses that curtail primary root growth under severe nutrient limitation.

Navigating the non-coding genome in heart development and Congenital Heart Disease.

Congenital Heart Disease (CHD) is characterised by a wide range of cardiac defects, from mild to life-threatening, which occur in babies worldwide. To date, there is no cure to CHD, however, progress in surgery has reduced its mortality allowing children affected by CHD to reach adulthood. In an effort to understand its genetic basis, several studies involving whole-genome sequencing (WGS) of patients with CHD have been undertaken and generated a great wealth of information. The majority of putative causative mutations identified in WGS studies fall into the non-coding part of the genome. Unfortunately, due to the lack of understanding of the function of these non-coding mutations, it is challenging to establish a causal link between the non-coding mutation and the disease. Thus, here we review the state-of-the-art approaches to interpret non-coding mutations in the context of CHD and address the following questions: What are the non-coding sequences important for cardiac function? Which technologies are used to identify them? Which resources are available to analyse them? What mutations are expected in these non-coding sequences? Learning from developmental process, what is their expected role in CHD?

Computational prediction of microRNAs in marine bacteria of the genus Thalassospira.

MicroRNAs (miRNAs) are key players in regulation of gene expression at post-transcription level in eukaryotic cells. MiRNAs have been intensively studied in plants, animals and viruses. The investigations of bacterial miRNAs have gained less attention, except for the recent studies on miRNAs derived from Streptococcus mutans ATCC 25175 and Escherichia coli DH10B. In this study, high-throughput sequencing approach was employed to investigate the miRNA population in bacteria of the genus Thalassospira using both the miRDeep2 and CID-miRNA methods. A total of 984 putative miRNAs were identified in 9 species of the genus Thalassospira using both miRDeep and CID-miRNA analyses. Fifty seven conserved putative miRNAs were found in different species of the genus Thalassospira, and up to 6 miRNAs were found to be present at different locations in the T. alkalitolerans JCM 18968T, T. lucentensis QMT2T and T. xianhensis P-4T. None of the putative miRNAs was found to share sequence to the reported miRNAs in E. coli DH10B and S. mutans ATCC 25175. The findings provide a comprehensive list of computationally identified miRNAs in 9 bacterial species of the genus Thalassospira and addressed the existing knowledge gap on the presence of miRNAs in the Thalassospira genomes.

Establishing a distributed national research infrastructure providing bioinformatics support to life science researchers in Australia.

EMBL Australia Bioinformatics Resource (EMBL-ABR) is a developing national research infrastructure, providing bioinformatics resources and support to life science and biomedical researchers in Australia. EMBL-ABR comprises 10 geographically distributed national nodes with one coordinating hub, with current funding provided through Bioplatforms Australia and the University of Melbourne for its initial 2-year development phase. The EMBL-ABR mission is to: (1) increase Australia's capacity in bioinformatics and data sciences; (2) contribute to the development of training in bioinformatics skills; (3) showcase Australian data sets at an international level and (4) enable engagement in international programs. The activities of EMBL-ABR are focussed in six key areas, aligning with comparable international initiatives such as ELIXIR, CyVerse and NIH Commons. These key areas-Tools, Data, Standards, Platforms, Compute and Training-are described in this article.

The molecular characterisation of mitochondrial DNA deficient oocytes using a pig model.

STUDY QUESTION: What are the molecular differences between mitochondrial DNA (mtDNA)-deficient and mtDNA-normal oocytes and how does mitochondrial supplementation alter these? SUMMARY ANSWER: Changes to DNA methylation in a 5' cytosine-phosphate-guanine 3' (CpG) island in the mtDNA-specific replication factor (DNA polymerase gamma (POLG)) of mtDNA-deficient oocytes mediates an increase in mtDNA copy number by the 2-cell stage that positively modulates the expression of nuclear genes, which affect cellular and metabolic processes, following autologous mitochondrial supplementation. WHAT IS KNOWN ALREADY: Too few copies of mtDNA in mature oocytes can lead to fertilisation failure or preimplantation embryo arrest. mtDNA-deficient oocytes that progress to blastocyst express genes associated with poor cellular and metabolic processes, transcriptional activation and mitochondrial biogenesis. STUDY DESIGN, SIZE, DURATION: Using a pig oocyte model, we assessed mtDNA-deficient and mtDNA-normal oocytes during in vitro maturation for mtDNA variants and levels of DNA methylation in POLG. We supplemented mtDNA-deficient oocytes with autologous populations of mitochondria to determine if there were changes to DNA methylation in POLG that coincided with increases in mtDNA copy number. We assessed metaphase II mtDNA-deficient and mtDNA-normal oocytes by RNA sequencing to identify differentially expressed genes and compared their profiles to blastocysts derived from mtDNA-normal, mtDNA-deficient and supplemented mtDNA-deficient oocytes. PARTICIPANTS/MATERIALS, SETTING, METHODS: mtDNA variant analysis (n = 24), mtDNA copy number (n = 60), POLG gene expression (n = 24), and RNA sequencing (n = 32 single; and 12 pooled cohorts of n = 5) were performed on oocytes and embryos. DNA methylation of a CpG island in POLG was determined quantitatively by pyrosequencing on oocytes to 2-cell embryos (n = 408). Bioinformatics tools were used to assess differences between mtDNA-normal and mtDNA-deficient oocytes and between mtDNA-normal and mtDNA-deficient oocytes and supplemented oocytes and their blastocyst stage equivalents. MAIN RESULTS AND THE ROLE OF CHANCE: Whilst mtDNA-deficient oocytes regulated variants less stringently during maturation (P < 0.05), there were no differences in the ratio of variants in mature-stage oocytes. However, mtDNA-normal mature oocytes had significantly more molecules affected due to their higher copy number (P < 0.0001). Normal mature oocytes differently DNA methylated a CpG island in POLG compared with mtDNA-deficient oocytes (P < 0.01). Supplementation of mtDNA-deficient oocytes modulated DNA methylation at this CpG island leading to a mtDNA replication event prior to embryonic genome activation inducing significant increases in mtDNA copy number. RNA-Seq identified 57 differentially expressed genes (false discovery rate (FDR) < 0.05) between the two cohorts of oocytes with blastocyst stage gene expression altered by supplementation of mtDNA-deficient oocytes (P < 0.05) including genes associated with metabolic disorders. One key factor was branched chain amino acid transaminase 2 (BCAT2), a regulator of amino acid metabolism and associated with diabetes. LARGE SCALE DATA: Sequence data are available on the NCBI Sequence Read Archive under the project number PRJNA422295. RNA sequencing data were deposited into NCBI Gene Expression Omnibus, under the accession number GSE108900. LIMITATIONS, REASONS FOR CAUTION: Whilst this work was conducted in a species that is highly relevant to human reproduction, the outcomes need to be tested in human oocytes and blastocysts prior to clinical application. WIDER IMPLICATIONS OF THE FINDINGS: The outcomes demonstrate a mechanism of action following mtDNA supplementation of mtDNA-deficient oocytes that results in improved gene expression at the blastocyst stage of development. STUDY FUNDING/COMPETING INTERESTS: This work was funded by OvaScience Inc. OvaScience did not influence the study design, analysis of results or interpretation of the data.

Insights into the neurochemical signature of the Innervation of Beige Fat.

OBJECTIVE: The potential for brown adipose tissue (BAT) to be targeted as a therapeutic option to combat obesity has been heightened by the discovery of a brown-like form of inducible "beige" adipose tissue in white fat which has overlapping structural and functional properties to "classical" BAT. The likelihood that both beige and brown fat are recruited functionally by neural mechanisms, taken together with the lack of a detailed understanding of the nature of changes in the nervous system when white adipose tissue (WAT) is transformed to brown, provides the impetus for this study. Here, we aim to identify whether there is a shift in the gene expression profile in neurons directly innervating inguinal white adipose tissue (iWAT) that has undergone "beiging" to a signature that is more similar to neurons projecting to BAT. METHODS: Two groups of rats, one housed at thermoneutrality (27 °C) and the other exposed to cold (8 °C) for 7 days, were killed, and their T13/L1 ganglia, stellate ganglion (T1/T2), or superior cervical ganglion (SCG, C2/3) removed. This approach yielded ganglia containing neurons that innervate either beiged white fat (8 °C for 7 days), inguinal WAT (27 °C for 7 days), BAT (both 27 °C and 8 °C for 7 days) or non-WAT (8 °C for 7 days), the latter included to isolate changes in gene expression that were more aligned with a response to cold exposure than the transformation of white to beige adipocytes. Bioinformatics analyses of RNA sequencing data was performed followed by Ingenuity Pathway Analysis (IPA) to determine differential gene expression and recruitment of biosynthetic pathways. RESULTS: When iWAT is "beiged" there is a significant shift in the gene expression profile of neurons in sympathetic ganglia (T13/L1) innervating this depot toward a gene neurochemical signature that is similar to the stellate ganglion projecting to BAT. Bioinformatics analyses of "beiging" related genes revealed upregulation of genes encoding neuropeptides proopiomelanocortin (POMC) and calcitonin-gene related peptide (CGRP) within ganglionic neurons. Treatment of differentiated 3T3L1 adipocytes with αMSH, one of the products cleaved from POMC, results in an elevation in lipolysis and the beiging of these cells as indicated by changes in gene expression markers of browning (Ucp1 and Ppargc1a). CONCLUSION: These data indicate that, coincident with beiging, there is a shift toward a "brown-like" neurochemical signature of postganglionic neurons projecting to inguinal white fat, an increased expression of POMC, and, consistent with a causative role for this prohormone in beiging, an αMSH-mediated increase in beige gene markers in isolated adipocytes.

Four simple recommendations to encourage best practices in research software.

Scientific research relies on computer software, yet software is not always developed following practices that ensure its quality and sustainability. This manuscript does not aim to propose new software development best practices, but rather to provide simple recommendations that encourage the adoption of existing best practices. Software development best practices promote better quality software, and better quality software improves the reproducibility and reusability of research. These recommendations are designed around Open Source values, and provide practical suggestions that contribute to making research software and its source code more discoverable, reusable and transparent. This manuscript is aimed at developers, but also at organisations, projects, journals and funders that can increase the quality and sustainability of research software by encouraging the adoption of these recommendations.

Towards an open, collaborative, reusable framework for sharing hands-on bioinformatics training workshops.

There is a clear demand for hands-on bioinformatics training. The development of bioinformatics workshop content is both time-consuming and expensive. Therefore, enabling trainers to develop bioinformatics workshops in a way that facilitates reuse is becoming increasingly important. The most widespread practice for sharing workshop content is through making PDF, PowerPoint and Word documents available online. While this effort is to be commended, such content is usually not so easy to reuse or repurpose and does not capture all the information required for a third party to rerun a workshop. We present an open, collaborative framework for developing and maintaining, reusable and shareable hands-on training workshop content.