Human Knockout Carriers: Dead, Diseased, Healthy, or Improved?

Whole-genome and whole-exome sequence data from large numbers of individuals reveal that we all carry many variants predicted to inactivate genes (knockouts). This discovery raises questions about the phenotypic consequences of these knockouts and potentially allows us to study human gene function through the investigation of homozygous loss-of-function carriers. Here, we discuss strategies, recent results, and future prospects for large-scale human knockout studies. We examine their relevance to studying gene function, population genetics, and importantly, the implications for accurate clinical interpretations

Extensive Proliferation of a Subset of Differentiated, yet Plastic, Medial Vascular Smooth Muscle Cells Contributes to Neointimal Formation in Mouse Injury and Atherosclerosis Models.

RATIONALE: Vascular smooth muscle cell (VSMC) accumulation is a hallmark of atherosclerosis and vascular injury. However, fundamental aspects of proliferation and the phenotypic changes within individual VSMCs, which underlie vascular disease, remain unresolved. In particular, it is not known whether all VSMCs proliferate and display plasticity or whether individual cells can switch to multiple phenotypes. OBJECTIVE: To assess whether proliferation and plasticity in disease is a general characteristic of VSMCs or a feature of a subset of cells. METHODS AND RESULTS: Using multicolor lineage labeling, we demonstrate that VSMCs in injury-induced neointimal lesions and in atherosclerotic plaques are oligoclonal, derived from few expanding cells. Lineage tracing also revealed that the progeny of individual VSMCs contributes to both alpha smooth muscle actin (aSma)-positive fibrous cap and Mac3-expressing macrophage-like plaque core cells. Costaining for phenotypic markers further identified a double-positive aSma+ Mac3+ cell population, which is specific to VSMC-derived plaque cells. In contrast, VSMC-derived cells generating the neointima after vascular injury generally retained the expression of VSMC markers and the upregulation of Mac3 was less pronounced. Monochromatic regions in atherosclerotic plaques and injury-induced neointima did not contain VSMC-derived cells expressing a different fluorescent reporter protein, suggesting that proliferation-independent VSMC migration does not make a major contribution to VSMC accumulation in vascular disease. CONCLUSIONS: We demonstrate that extensive proliferation of a low proportion of highly plastic VSMCs results in the observed VSMC accumulation after injury and in atherosclerotic plaques. Therapeutic targeting of these hyperproliferating VSMCs might effectively reduce vascular disease without affecting vascular integrity.This work was funded by the British Heart Foundation grants to HFJ (PG/12/86/29930, FS/15/38/31516). HFJ and MRB acknowledge support from the BHF Oxbridge Centre of Regenerative Medicine [RM/13/3/30159] and the BHF Cambridge Centre of Research Excellence [RE/13/6/30180]. BDS acknowledges the support of the Wellcome Trust [098357/Z/12/Z].This is the final version of the article. It first appeared from the American Heart Association via https://doi.org/10.1161/CIRCRESAHA.116.30979

Inferring Developmental Stage Composition from Gene Expression in Human Malaria

In the current era of malaria eradication, reducing transmission is critical. Assessment of transmissibility requires tools that can accurately identify the various developmental stages of the malaria parasite, particularly those required for transmission (sexual stages). Here, we present a method for estimating relative amounts of Plasmodium falciparum asexual and sexual stages from gene expression measurements. These are modeled using constrained linear regression to characterize stage-specific expression profiles within mixed-stage populations. The resulting profiles were analyzed functionally by gene set enrichment analysis (GSEA), confirming differentially active pathways such as increased mitochondrial activity and lipid metabolism during sexual development. We validated model predictions both from microarrays and from quantitative RT-PCR (qRT-PCR) measurements, based on the expression of a small set of key transcriptional markers. This sufficient marker set was identified by backward selection from the whole genome as available from expression arrays, targeting one sentinel marker per stage. The model as learned can be applied to any new microarray or qRT-PCR transcriptional measurement. We illustrate its use in vitro in inferring changes in stage distribution following stress and drug treatment and in vivo in identifying immature and mature sexual stage carriers within patient cohorts. We believe this approach will be a valuable resource for staging lab and field samples alike and will have wide applicability in epidemiological studies of malaria transmission

Health and population effects of rare gene knockouts in adult humans with related parents.

Examining complete gene knockouts within a viable organism can inform on gene function. We sequenced the exomes of 3222 British adults of Pakistani heritage with high parental relatedness, discovering 1111 rare-variant homozygous genotypes with predicted loss of function (knockouts) in 781 genes. We observed 13.7% fewer homozygous knockout genotypes than we expected, implying an average load of 1.6 recessive-lethal-equivalent loss-of-function (LOF) variants per adult. When genetic data were linked to the individuals' lifelong health records, we observed no significant relationship between gene knockouts and clinical consultation or prescription rate. In this data set, we identified a healthy PRDM9-knockout mother and performed phased genome sequencing on her, her child, and control individuals. Our results show that meiotic recombination sites are localized away from PRDM9-dependent hotspots. Thus, natural LOF variants inform on essential genetic loci and demonstrate PRDM9 redundancy in humans.The study was funded by the Wellcome Trust (WT102627 and WT098051), Barts Charity (845/1796), Medical Research Council (MR/M009017/1). This paper presents independent research funded by the National Institute for Health Research (NIHR) under its Collaboration for Applied Health Research and Care (CLAHRC) for Yorkshire and Humber. Core support for Born in Bradford is also provided by the Wellcome Trust (WT101597). V.N. was supported by the Wellcome Trust PhD Studentship (WT099769). D.G.M. and K.K. were supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number R01GM104371. E.R.M. is funded by NIHR Cambridge Biomedical Research Centre. H.H. is supported by awards to establish the Farr Institute of Health Informatics Research, London, from the Medical Research Council, Arthritis Research UK, British Heart Foundation, Cancer Research UK, Chief Scientist Office, Economic and Social Research Council, Engineering and Physical Sciences Research Council, NIHR, National Institute for Social Care and Health Research, and Wellcome Trust.This is the author accepted manuscript. The final version is available from the American Association for the Advancement of Science via https://doi.org/10.1126/science.aac862

High polygenic risk score is a risk factor associated with colorectal cancer based on data from the UK Biobank.

Colorectal cancer (CRC) is a common cancer among both men and women and is one of the leading causes of cancer death worldwide. It is important to identify risk factors that may be used to help reduce morbidity and mortality of the disease. We used a case-control study design to explore the association between CRC, polygenic risk scores (PRS), and other factors. We extracted data about 2,585 CRC cases and 9,362 controls from the UK Biobank, calculated the PRS for these cases and controls based on 140 single nucleotide polymorphisms, and performed logistic regression analyses for the 11,947 cases and controls, for an older group (ages 50+), and for a younger group (younger than 50). Five significant risk factors were identified when all 11,947 cases and controls were considered. These factors were, in descending order of the values of the adjusted odds ratios (aOR), high PRS (aOR: 2.70, CI: 2.27-3.19), male sex (aOR: 1.52, CI: 1.39-1.66), unemployment (aOR: 1.47, CI: 1.17-1.85), family history of CRC (aOR: 1.44, CI: 1.28-1.62), and age (aOR: 1.01, CI: 1.01-1.02). These five risk factors also remained significant in the older group. For the younger group, only high PRS (aOR: 2.87, CI: 1.65-5.00) and family history of CRC (aOR: 1.73, CI: 1.12-2.67) were significant risk factors. These findings indicate that genetic risk for the disease is a significant risk factor for CRC even after adjusting for family history. Additional studies are needed to examine this association using larger samples and different population groups

The formation of human populations in South and Central Asia

By sequencing 523 ancient humans, we show that the primary source of ancestry in modern South Asians is a prehistoric genetic gradient between people related to early hunter-gatherers of Iran and southeast Asia. Following the Indus Valley Civilization’s decline, they mixed with people in the southeast to form one of the two main ancestral populations of South Asia whose direct descendants live in southern India. Simultaneously, they mixed with descendants of Steppe pastoralists who spread via Central Asia after 4000 years ago to form the other main ancestral population. The Steppe ancestry in South Asia has the same profile as that in Bronze Age Eastern Europe, tracking a movement of people that affected both regions and that likely spread the unique shared features shared between Indo-Iranian and Balto-Slavic languages

A Deep Learning Approach for Improving Two-Photon Vascular Imaging Speeds

A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based (PSSR Res-U-Net architecture) algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acquiring low- and high-resolution training pairs without compromising vectorization outcomes, and thus open the possibility of utilizing such approaches for other MPM tasks where collecting training data is challenging. We applied our approach to images with large fields of view from a mouse model and show that our method generalizes across imaging depths, disease states and other differences in neurovasculature. Our pretrained models and lightweight architecture can be used to reduce MPM imaging time by up to fourfold without any changes in underlying hardware, thereby enabling deployability across a range of settings

Millennia-old coral holobiont DNA provides insight into future adaptive trajectories

Ancient DNA (aDNA) has been applied to evolutionary questions across a wide variety of taxa. Here, for the first time, we utilized aDNA from millennia-old fossil coral fragments to gain new insights into a rapidly declining western Atlantic reef ecosystem. We sampled four Acropora palmata fragments (dated 4215 BCE to 1099 CE) obtained from two Florida Keys reef cores. From these samples, we established that it is possible both to sequence aDNA from reef cores and place the data in the context of modern-day genetic variation. We recovered varying amounts of nuclear DNA exhibiting the characteristic signatures of aDNA from the A. palmata fragments. To describe the holobiont sensu lato, which plays a crucial role in reef health, we utilized metagenome-assembled genomes as a reference to identify a large additional proportion of ancient microbial DNA from the samples. The samples shared many common microbes with modern-day coral holobionts from the same region, suggesting remarkable holobiont stability over time. Despite efforts, we were unable to recover ancient Symbiodiniaceae reads from the samples. Comparing the ancient A. palmata data to whole-genome sequencing data from living acroporids, we found that while slightly distinct, ancient samples were most closely related to individuals of their own species. Together, these results provide a proof-of-principle showing that it is possible to carry out direct analysis of coral holobiont change over time, which lays a foundation for studying the impacts of environmental stress and evolutionary constraints.publishe

Deep learning based phenotyping of medical images improves power for gene discovery of complex disease

Abstract Electronic health records are often incomplete, reducing the power of genetic association studies. For some diseases, such as knee osteoarthritis where the routine course of diagnosis involves an X-ray, image-based phenotyping offers an alternate and unbiased way to ascertain disease cases. We investigated this by training a deep-learning model to ascertain knee osteoarthritis cases from knee DXA scans that achieved clinician-level performance. Using our model, we identified 1931 (178%) more cases than currently diagnosed in the health record. Individuals diagnosed as cases by our model had higher rates of self-reported knee pain, for longer durations and with increased severity compared to control individuals. We trained another deep-learning model to measure the knee joint space width, a quantitative phenotype linked to knee osteoarthritis severity. In performing genetic association analysis, we found that use of a quantitative measure improved the number of genome-wide significant loci we discovered by an order of magnitude compared with our binary model of cases and controls despite the two phenotypes being highly genetically correlated. In addition we discovered associations between our quantitative measure of knee osteoarthritis and increased risk of adult fractures- a leading cause of injury-related death in older individuals-, illustrating the capability of image-based phenotyping to reveal epidemiological associations not captured in the electronic health record. For diseases with radiographic diagnosis, our results demonstrate the potential for using deep learning to phenotype at biobank scale, improving power for both genetic and epidemiological association analysis