The potassium-chloride co-transporter KCC2, encoded by SLC12A5, plays a fundamental role in fast synaptic inhibition by maintaining a hyperpolarizing gradient for chloride ions. KCC2 dysfunction has been implicated in human epilepsy, but to date, no monogenic KCC2-related epilepsy disorders have been described. Here we show recessive loss-of-function SLC12A5 mutations in patients with a severe infantile-onset pharmacoresistant epilepsy syndrome, epilepsy of infancy with migrating focal seizures (EIMFS). Decreased KCC2 surface expression, reduced protein glycosylation and impaired chloride extrusion contribute to loss of KCC2 activity, thereby impairing normal synaptic inhibition and promoting neuronal excitability in this early-onset epileptic encephalopathy.

Green plant leaves have always fascinated biologists as hosts for photosynthesis and providers of basic energy to many food webs. Today, comprehensive databases of gene expression data enable us to apply increasingly more advanced computational methods for reverse-engineering the regulatory network of leaves, and to begin to understand the gene interactions underlying complex emergent properties related to stress-response and development. These new systems biology methods are now also being applied to organisms such as Populus, a woody perennial tree, in order to understand the specific characteristics of these species. We present a systems biology model of the regulatory network of Populus leaves. The network is reverse-engineered from promoter information and expression profiles of leaf-specific genes measured over a large set of conditions related to stress and developmental. The network model incorporates interactions between regulators, such as synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms, and is therefore able to identify new regulators of leaf development not found by traditional genomics methods based on pair-wise expression similarity. The approach is shown to explain available gene function information and to provide robust prediction of expression levels in new data. We also use the predictive capability of the model to identify condition-specific regulation as well as conserved regulation between Populus and Arabidopsis. We outline a computationally inferred model of the regulatory network of Populus leaves, and show how treating genes as interacting, rather than individual, entities identifies new regulators compared to traditional genomics analysis. Although systems biology models should be used with care considering the complexity of regulatory programs and the limitations of current genomics data, methods describing interactions can provide hypotheses about the underlying cause of emergent properties and are needed if we are to identify target genes other than those constituting the "low hanging fruit" of genomic analysis.

Autoantibodies against type I interferons (IFN) alpha (α) and omega (ω), and interleukins (IL) 17 and 22 are a hallmark of autoimmune polyendocrine syndrome type 1 (APS-1), caused by mutations in the autoimmune regulator (AIRE) gene. Such antibodies are also seen in a number of monogenic immunodeficiencies. To determine whether screening for cytokine autoantibodies (anti-IFN-ω and anti-IL22) can be used to identify patients with monogenic immune disorders. A novel ELISA assay was employed to measure IL22 autoantibodies in 675 patients with autoimmune primary adrenal insufficiency (PAI) and a radio immune assay (RIA) was used to measure autoantibodies against IFN-ω in 1778 patients with a variety of endocrine diseases, mostly of autoimmune aetiology. Positive cases were sequenced for all coding exons of the AIRE gene. If no AIRE mutations were found, we applied next generation sequencing (NGS) to search for mutations in immune related genes. We identified 29 patients with autoantibodies against IFN-ω and/or IL22. Of these, four new APS-1 cases with disease-causing variants in AIRE were found. In addition, we identified two patients with pathogenic heterozygous variants in CTLA4 and NFKB2, respectively. Nine rare variants in other immune genes were identified in six patients, although further studies are needed to determine their disease-causing potential. Screening of cytokine autoantibodies can efficiently identify patients with previously unknown monogenic and possible oligogenic causes of autoimmune and immune deficiency diseases. This information is crucial for providing personalised treatment and follow-up of patients and their relatives.

The origin of domestic dogs is poorly understood [1-15], with suggested evidence of dog-like features in fossils that predate the Last Glacial Maximum [6, 9, 10, 14, 16] conflicting with genetic estimates of a more recent divergence between dogs and worldwide wolf populations [13, 15, 17-19]. Here, we present a draft genome sequence from a 35,000-year-old wolf from the Taimyr Peninsula in northern Siberia. We find that this individual belonged to a population that diverged from the common ancestor of present-day wolves and dogs very close in time to the appearance of the domestic dog lineage. We use the directly dated ancient wolf genome to recalibrate the molecular timescale of wolves and dogs and find that the mutation rate is substantially slower than assumed by most previous studies, suggesting that the ancestors of dogs were separated from present-day wolves before the Last Glacial Maximum. We also find evidence of introgression from the archaic Taimyr wolf lineage into present-day dog breeds from northeast Siberia and Greenland, contributing between 1.4% and 27.3% of their ancestry. This demonstrates that the ancestry of present-day dogs is derived from multiple regional wolf populations.

Promoter mutations of the telomerase reverse transcriptase (TERT) gene occur frequently in thyroid carcinoma (TC), including papillary (PTC) and anaplastic subtypes (ATC). Given that the ETS family transcription factors GABPA and GABPB1 activate the mutant TERT promoter and induce TERT expression for telomerase activation, GABPB1 has been proposed as a cancer therapeutic target to inhibit telomerase. Here, we sought to determine the role of GABPB1 in TC pathogenesis. In TC-derived cells carrying the mutated TERT promoter, GABPB1 knockdown led to diminished TERT expression but significantly increased invasive potentials in vitro and metastatic potential in a xenograft zebrafish model and altered expression of markers for epithelial-to-mesenchymal transition. GABPB1 expression was downregulated in aggressive TCs. Low GABPB1 expression correlated with its promoter hypermethylation, which in turn was also associated with shorter disease-free survival. Consistently, DNA methylation inhibitors enhanced GABPB1 expression, as observed upon reduced promoter methylation. Our results suggest that GABPB1 is required for TERT expression and telomerase activation, but itself serves as a tumor suppressor to inhibit TC progression. Furthermore, aberrant DNA methylation leads to GABPB1 silencing, thereby promoting TC aggressiveness. Thus, caution is needed if targeting GABPB1 for cancer therapy is considered.

High-throughput sequencing platforms mainly produce short-read data, resulting in a loss of phasing information for many of the genetic variants analysed. For certain applications, it is vital to know which variant alleles are connected to each individual DNA molecule. Here we demonstrate a method for massively parallel barcoding and phasing of single DNA molecules. First, a primer library with millions of uniquely barcoded beads is generated. When compartmentalized with single DNA molecules, the beads can be used to amplify and tag any target sequences of interest, enabling coupling of the biological information from multiple loci. We apply the assay to bacterial 16S sequencing and up to 94% of the hypothesized phasing events are shown to originate from single molecules. The method enables use of widely available short-read-sequencing platforms to study long single molecules within a complex sample, without losing phase information.

The cell cycle, over which cells grow and divide, is a fundamental process of life. Its dysregulation has devastating consequences, including cancer1-3. The cell cycle is driven by precise regulation of proteins in time and space, which creates variability between individual proliferating cells. To our knowledge, no systematic investigations of such cell-to-cell proteomic variability exist. Here we present a comprehensive, spatiotemporal map of human proteomic heterogeneity by integrating proteomics at subcellular resolution with single-cell transcriptomics and precise temporal measurements of individual cells in the cell cycle. We show that around one-fifth of the human proteome displays cell-to-cell variability, identify hundreds of proteins with previously unknown associations with mitosis and the cell cycle, and provide evidence that several of these proteins have oncogenic functions. Our results show that cell cycle progression explains less than half of all cell-to-cell variability, and that most cycling proteins are regulated post-translationally, rather than by transcriptomic cycling. These proteins are disproportionately phosphorylated by kinases that regulate cell fate, whereas non-cycling proteins that vary between cells are more likely to be modified by kinases that regulate metabolism. This spatially resolved proteomic map of the cell cycle is integrated into the Human Protein Atlas and will serve as a resource for accelerating molecular studies of the human cell cycle and cell proliferation.