One in five pregnant women suffer from gestational complications, prevalently driven by placental malfunction. Using RNASeq, we analyzed differential placental gene expression in cases of normal gestation, late-onset preeclampsia (LO-PE), gestational diabetes (GD) and pregnancies ending with the birth of small-for-gestational-age (SGA) or large-for-gestational-age (LGA) newborns (n = 8/group). In all groups, the highest expression was detected for small noncoding RNAs and genes specifically implicated in placental function and hormonal regulation. The transcriptome of LO-PE placentas was clearly distinct, showing statistically significant (after FDR) expressional disturbances for hundreds of genes. Taqman RT-qPCR validation of 45 genes in an extended sample (n = 24/group) provided concordant results. A limited number of transcription factors including LRF, SP1 and AP2 were identified as possible drivers of these changes. Notable differences were detected in differential expression signatures of LO-PE subtypes defined by the presence or absence of intrauterine growth restriction (IUGR). LO-PE with IUGR showed higher correlation with SGA and LO-PE without IUGR with LGA placentas. Whereas changes in placental transcriptome in SGA, LGA and GD cases were less prominent, the overall profiles of expressional disturbances overlapped among pregnancy complications providing support to shared placental responses. The dataset represent a rich catalogue for potential biomarkers and therapeutic targets.

Highlights d Molecular subtypes and genetics shape immune landscape in hematological malignancies d Cytotoxic T/NK cell infiltration in MDS-like AML with TP53 mutations and ABC DLBCL d Methylation changes suppress HLA genes in AML and induce cancer antigens in myeloma d Cancer type-specific targets such as VISTA in myeloid and CD70 in lymphoid cancers

We report the immunogenomic landscape of >10,000 hematological malignancies by integrating large-scale genomic, epigenomic, and transcriptomic datasets in this article. During its preparation, we submitted an incorrect version of Figure 1A, in which the numbers of the cases in the Hemap dataset were incorrect (1,288 and 4,293 lymphoma and leukemia samples, respectively; the correct numbers are 1,300 and 4,281). Similarly, in Figure S1A, the number of cell lines in CCLE dataset was incorrect (CHL n = 9 changed to n = 8, and unknown n = 7 is now included). The number of cases reported in the first paragraph of the Results section has also been corrected to reflect these revisions (''We used 7,092 samples from 36 hematological malignancies, with 770 healthy donor hematological cell populations and 610 cell lines as controls [Pö lö nen et al., 2019], to comprehensively analyze immunological properties in hematological cancer transcriptomes [Figures 1A and S1A; Table S1]''). These errors do not affect any of the data or conclusions in the article, and the figures have been revised in the online and printed versions of the paper, which differ from the version originally published online on July 9, 2020. We apologize for any confusion these errors may have caused.

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The whole genome approach enables the characterization of all components of any given biological pathway. Moreover, it can help to uncover all the metabolic routes for any molecule. Here we have used the genome of Drosophila melanogaster to search for enzymes involved in the metabolism of fucosylated glycans. Our results suggest that in the fruit fly GDP-fucose, the donor for fucosyltransferase reactions, is formed exclusively via the de novo pathway from GDP-mannose through enzymatic reactions catalyzed by GDP-D-mannose 4,6-dehydratase (GMD) and GDP-4-keto-6-deoxy-Dmannose 3,5-epimerase/4-reductase (GMER, also known as FX in man). The Drosophila genome does not have orthologs for the salvage pathway enzymes, i.e. fucokinase and GDP-fucose pyrophosphorylase synthesizing GDP-fucose from fucose. In addition we identified two novel fucosyltransferases predicted to catalyze ␣1,3-and ␣1,6-specific linkages to the GlcNAc residues on glycans. No genes with the capacity to encode ␣1,2-specific fucosyltransferases were found. We also identified two novel genes coding for O-fucosyltransferases and a gene responsible for a fucosidase enzyme in the Drosophila genome. Finally, using the Drosophila CG4435 gene, we identified two novel human genes putatively coding for fucosyltransferases. This work can serve as a basis for further whole-genome approaches in mapping all possible glycosylation pathways and as a basic analysis leading to subsequent experimental studies to verify the predictions made in this work.