SUPPLEMENTAL MATERIAL. Characterization and discovery of novel mirnas and mornas in JAK2V617F. mutated SET2 cells

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1 SUPPLEMENTAL MATERIAL Characterization and discovery of novel mirnas and mornas in JAK2V617F mutated SET2 cells Stefania Bortoluzzi 1*, Andrea Bisognin 1*, et al. 1

2 Supplemental methods Short RNA library construction for sequencing Total RNA from SET2 cells (DSMZ, Braunschweig, Germany) was size-fractionated on a 15% trisborate-edta-urea polyacrylamide gel, RNA fragments nts length were isolated from the gel, quantified, and ethanol precipitated. The SRA 5 adapter (Illumina) was ligated to the RNA fragments with T4 RNA ligase (Promega), followed by 15% tris-borate-edta-urea polyacrylamide gel size-fractionation. The RNA fragments of ~41-76 nts length were isolated. The SRA 3 adapter (Illumina) ligation was then performed, followed by a second size-fractionation and recovery of RNA fragments of ~64-99 nts length. The ligated RNA fragments were reverse transcribed to singlestranded cdnas using M- MLV (Invitrogen) with RT-primers recommended by Illumina. The cdnas were amplified with pfx DNA polymerase (Invitrogen) in 20 cycles of PCR using Illumina s small RNA primers set. PCR products were then purified on a 12% TBE polyacrylamide gel and a slice of gel of ~ bps was excised. This fraction was eluted and the recovered cdnas were precipitated and quantified on Nanodrop (Thermo Scientific) and on TBS-380 mini-fluorometer (Turner Biosystems) using Picogreen (Invitrogen). Computational pipeline A computational pipeline was set up using the Scons build tool ( as skeleton It includes third party libraries and programs and in house developed code and aims to analyze in a reproducible way short RNA sequencing data using up to date metadata from human genome sequence and mirbase ( As shown in Figure S1, the pipeline includes raw data pre-processing, different filtering steps before and after mapping to reference sequences, as well as methods for known mirnas quantification, isomir characterization, and for mirna and other short RNA discovery. 2

3 Raw data pre-processing and quality filters Sequencing data were pre-processed to remove the adapter sequence and a first quality filter was applied using FastX clipper. Reads shorter than 15 nt, without adapter, and/or with uncalled positions (N) were discarded. Since Illumina Base quality may underestimate the actual sequencing error associated to called bases (Dohm et al., 2008) we used a conservative methodology by implementing a second, more stringent quality filter based on reads length, single base quality and average sequence quality over the reads, by using HTSeq python package ( Reads longer than 26 nt were discarded and only reads with average sequence quality of at least 30 and with less than 2 nt with base quality lower than 20 were considered for further analysis. Other RNAs and repeats filtering Reads were mapped to reference sequences using Bowtie, run on multiple processors 1. Unique sequences obtained collapsing reads with identical sequence were first aligned using Bowtie, allowing only exact matches, to the human genome sequence (hg19), and the number of mapping loci per unique sequence was recorded. Then, we mapped read sequences to known human mirna hairpin sequences from mirbase (Release 17: 27 April 2011). Since non-canonical Drosha processing has been reported, we considered the genomic region surrounding the hairpin, namely the sequence containing the hairpin sequence plus 30 nt before and after the mirna precursor, obtaining an extended hairpin sequence. For each unique sequence, we compared the number of mapping positions in the whole human genome with the number of mapping positions in reference mirna hairpins to discard contaminations from non-mirna RNA genes and repeats, tolerating for possibly unknown mirna loci. Reads belonging to unique sequences mapping in more than 5 genomic loci out of mirna hairpins were discarded. 3

4 Figure S1. Outline of the study. 4

5 Reads mapping to hairpin precursor sequences To obtain accurate reads mapping to hairpin precursor sequences, we considered sequence and quality information. Bowtie reads to extended hairpins alignments were obtained with the following criteria: at most two mismatches were allowed, the entire length of the read was used as seed for the alignment. Bowtie was run in best y mode, which is the slower but accurate mode. Indeed, the option strata was set to obtain the complete set of alignments of the best stratum (0, 1 or 2 mismatches) irrespectively of the total number of equally good alignments. For instance, if a read aligned exactly (without mismatches) in 3 positions of reference sequences, then 3 alignments were reported, and alignments with 1 or 2 mismatches, if present, were neglected. On the other hand, if for a given read we saw one or more alignments with 2 mismatches, this meant that no alignments existed with 0 or 1 mismatch. Alignment results were parsed using custom scripts and functions of the HTseq package. Read alignment results were integrated with information about known mirnas positions relative to hairpin sequences and reads matching hairpin precursors in the region corresponding to mature mirnas were classified into the following categories: - Exact (the read matches the reference sequence without mismatches and the length is exactly the same of the mature mirna). - Longer/Shorter (the read matches the reference sequence without mismatches but it is shorter/longer than the known official mature mirna of at most 3 nt at the 5 and/or at the 3 ). This category of reads may occur as a consequence of alternative processing of hairpin and/or mature sequence trimming 5 or 3. - Mismatch alignments (the read aligns with one or two mismatches, and no better alignments are found with other mirnas). Mismatch alignments were considered only if a unique mismatch was observed, tolerating for sequencing errors and SNPs existence, or in case two mismatches were observed both after the end of the complete alignment to the mature sequence. This category may identify post-processing addition of nucleotides. Multiple mapping-corrected read count For each category of read alignment (Exact, Longer/Shorter, 1-Mismatch, 2-3 -Mismatches), we 5

6 calculated the number of reads mapping to different number of reference sequences, considering both unique mature mirnas and unique hairpins. Considering the multiple mapping issue, a fraction of considered reads mapped to more than one reference sequence. It is worth notice that identical mature mirnas belonging to different hairpins precursors are considered only once. Thus, as expected due to the existence of identical mature mirnas produced by different hairpin precursors and loci, a more extensive multiple mapping against precursors rater than against mature mirnas was observed. If the per mirna reads count is used for mirna expression level quantification as such, without considering the problem of multiple matching, a considerable distortion may be introduced. For instance, a given read mapping to n different mirnas might be counted once for expression quantification of all these mirnas. Paradoxically, in this way, the sum of read counts over all mirnas may exceed the number of sequenced reads. In consideration of above reported results about multiple mapping of reads involved in different categories of read-to- hairpins alignment, a rigorous method for mirna expression quantification from reads count needs to adjust for the multiple mapping phenomenon. Thus, in this study, a corrected read count was calculated as c=x/n, where x is the raw read count per reference sequence pertaining to a given read and n is the number of different reference sequences showing equally good alignments with the read. The corrected read count was then used for all the following analyses, namely for quantification of both the individual RNA sequences expression level and the relative contribution of mirna sequence variants (isomirs). Expressed RNA elements identification New mirnas and other short RNAs derived from mirna precursors were identified by considering only exact alignments of read sequences with extended hairpin sequences. We defined expressed RNAs elements (ERE) those hairpin regions showing blocks of alignments that form a read group according to the following rules. Aligned reads were associated to start and end coordinates respectively to the extended hairpin. They were evaluated according to start coordinate, in 5 to 3 direction, to create one or more group or reads, by including in each group the maximum number of subsequent reads each showing a start coordinate within 4 nucleotides from the previous one. The per group sum of read counts was associated to each ERE, identified as a region of the extended hairpin. In this way, EREs that are distinct according to sequencing 6

7 information, but partially overlapping respectively to the precursor, can be identified; the latter can possibly derive by alternative processing,. Then, EREs corresponding to known mature mirnas were recognized by identifying those elements with central nucleotides included in the region annotated as a known mirna. The identification of expressed mature mirnas is trivial for hairpins with two annotated mature mirna products. For hairpins containing only one annotated mature mirna, RNA fold was used to define the most probable hairpin structure and to define the region complementary to the known mirna, thus identifying the most probable mirna duplex. Then, the overlap between this region and expressed RNA objects was defined, resulting in the identification of new mirnas. In this way, the localization of a mirna pair per hairpin was defined and then used as anchors to identify the nature of possible additional EREs observed in the same hairpin. Specifically, expressed RNA elements with central nucleotide localized respectively upstream of the region covered by the 5 mature mirna or downstream of the region covered by the 3 mature mirna were considered 5 -mornas and 3 mornas. Similarly, expressed loops were identified as expressed RNA elements localized between the two mirnas. New mirna target prediction Genes target of new mirnas were predicted by TargetScan using as input new mirna sequences and the set of 3 UTR sequences of all human transcripts and orthologs in 23 species (TargetScan database v5.2). TargetScan groups mirnas (intra- and inter species) in a family if they have conserved 8mer and 7mer sites representing the seed region, required by the prediction algorithm. For each novel mirna, a family was created aggregating to all known mirnas the novel mirna satisfying a custom similarity condition. Specifically, for each novel mirna we performed a pair-wise alignment with all known mirnas, using ClustalW2 algorithm, from abovementioned species set. If the number of matches between the 5 regions of mirna pairs exceeded the half-length of the shorter mirna minus 3, the novel and the known mirnas were included in a custom family. The process was repeated iteratively. Then for each custom family we performed a multiple alignment with ClustalW2 to find the common substring of 7nt in the 5 of all mirnas sequences, which was used as seed sequence for target predictions. 7

8 Determination of Expressed RNA elements conservation score in placental mammals The complete genome wide track phastcons46wayplacental.wib for assembly hg19 was downloaded from UCSC genome browser ftp site and was then queried with the hgwiggle command line tool to obtain a conservation score for each nucleotide of expressed RNA elements. Nucleotide-level scores were summarized with arithmetic mean to associate a single Phastcons conservation score for placental mammals to every expressed mirnas and mornas. A Wilcoxon rank sum test or a Wilcoxon signed rank test, where appropriate, was used to assess significance of difference in conservation score between groups. mirna expression profiling in SET2 cells treated with JAK1/JAK2 inhibitor INC424/Ruxolitinib Exponentially growing SET2 cells were cultured in medium supplemented with 10% fetal bovine serum in the presence or the absence (control cells) of 160, 800 e 1,600 nm concentrations of the JAK1 and JAK2 inhibitor INC424/Ruxolitinib for 3 and 6 h. Triplicate cultures were prepared for each drug concentration and culture time. Total cellular RNA, including small RNAs, was extracted using RNeasy Micro kit (Qiagen, Valencia, CA). Disposable RNA chips (Agilent RNA 6000 Nano LabChip kit, Agilent Technologies, Waldbrunn, Germany) were used to determine the purity/integrity of RNA samples using Agilent 2100 Bioanalyzer. NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE) was used to evaluate the RNA sample concentration, and 260/280 and 260/230 nm ratios were considered to verify the purity of total RNA. Total RNA (500ng for samples) was labeled using the FlashTag Biotin HSR kit (Genisphere) and hybridized to the Affymetrix Genechip mirna array 2.0 using manufacturer's protocols. Image files from scanning (.dat and.cel files) were generated with Affymetrix Expression Console package. Signal reconstruction from raw array data and data normalization were obtained using RMA (Affy library of Bioconductor) with a custom procedure (background subtraction step performed considering all species and controls of the chip; normalization and summarization steps considering only human probesets). An expression matrix including 1,100 human micrornas was obtained and used to identify those mirnas whose expression level was affected by the dose and time factor. A repeated measure ANOVA model was fitted for each mirna with dose (levels of INC424/Ruxolitinib) as a between-groups factor and time (3h and 6 h) as a within-group factor. mirnas for which the dose factor resulted significant in a F test at α<0.05 were selected and contrasts were examined with Tukey Honest Significance Difference method. 8

9 Supplemental Results Raw sequencing data preprocessing The Illumina GAIIx sequencing of the small RNA library from SET-2 cells produced 32,760,003 reads. The clipping and first quality control phase discarded 651,000 reads (2%) that were shorter than 15 nt, not clipped, including only the adapter sequence, or with uncalled bases. We observed that the base quality considerably decreased in the last positions of reads: the average per nt quality calculated over all reads drops below 30 in positions over 29/30 nt from the 5 of the reads. Considering that the reportedly observed error rates might considerably exceed the expected/theoretical values (e.g. for nt with quality of 30 true error rates were observed times higher than the expected 1/1000 error rate 2. A second quality filter was applied: we discarded the reads longer than 26 nt as well those with more than one nt with base quality under 20 and/or with average sequence quality lower than 30. In total, 3,699,580 reads were discarded in this phase, producing a cleaned dataset of 28,399,413 reads that represented 87% of the raw reads. Finally, only reads whose sequence was represented at least 10 times in the sequencing dataset were considered for the following analyses, obtaining a set of 27,317,905 reads. Reads mapping to reference sequences Sequence reads represent the whole population of cellular short RNAs. In this study we focus on the fraction of short RNAs produced from the processing of micrornas hairpin precursors. All known human mirna sequences from mirbase (Release 17: Apr 2011) were considered, including 1,421 known hairpin precursors, corresponding to 1,731 known mature mirna sequences. This collection of mirnas includes a number of mirnas recently discovered by deep sequencing and not listed in the previous mirbase releases. As said in Methods, the genomic sequence surrounding the hairpin precursors (30 nt before and after) was considered as an extended mirna hairpin precursor sequence that was then used as reference for the following analyses. In the first mapping phase sequence reads were aligned both to the human genome and to 9

10 extended hairpins by considering only exact matches, with the aim of discarding contaminations from non- mirna RNA genes and repeats, but tolerating for possibly unknown mirna loci. 19,870,706 reads were mapped to the human genome sequences, whereas 22,142,347 were mapped to extended hairpins. Out of the latter, 53,599 (0.2%) were discarded since mapping in more than 5 genomic loci out of mirna hairpins. In this way, we obtained a final set of 2,208,8748 reads whose accurate mapping to extended hairpin was the starting point for known mirnas identification, quantification and isomirs definition, mirna discovery as well for the detection of other mirna-associated expressed RNAs. Known mirnas identification and quantification and new mirnas discovery are based on exact alignments. IsomiRs analysis is based on the whole considered read alignments. Hairpin mapping and multiple mapping-corrected reads count We reasoned that part of hairpin-derived RNAs might differ from (and thus imperfectly align to) the reference sequence (i.e. the sequence from which they are derived, as the mirna hairpin precursor) due to the variable contribution of well known phenomena, which are listed above according to a speculative decreasing order of frequency: sequencing errors, intraspecific variability (SNPs), mature mirna sequence heterogeneity (isomirs) due to incorrect/unconventional/alternative/regulated Drosha processing, 5 and 3 trimming and 3 nucleotide substitutions. isomirs definition regarding known mirnas is based on exact reads alignments as well as on specific alignment categories (with mismatches and tolerating for reads being slightly longer or shorter that matching precursor sequence, as detailed in Methods) to tolerate for sequencing errors and SNPs existence, allowing discovering of postprocessing addition of nucleotides, alternative processing of hairpin and/or mature sequence trimming 5 or 3. The information contained in alignments regarding reads matching hairpin precursors in the region corresponding to known mature mirnas was used to identify known mirnas expressed in SET2 cells, and to estimate their expression level, as well as to characterize sequence variants observed for each mirna (isomirs). Before that, we used alignment data to understand more about the problem of reads showing multiple mapping to different reference sequences, representing distinct genomic loci, to envisage and apply a working solution for a multiple- 10

11 mapping aware sequence- based estimate of mirna expression level and variant contribution. The problem of mapping quality was considerably overlooked by different studies exploiting RNAs deep sequencing for mirnas and isomirs discovery. For each category of read alignment (Exact, Longer/Shorter, 1-Mismatch, 2-3 -Mismatches), we calculated the number of reads mapping to different number of reference sequences, considering both unique mature mirnas and unique hairpins (Table S1). Reads matching mirnas exactly accounted for 42% of all considered matching reads, whereas 48% of matching reads belonged to the shorter/longer class, aligning to a region of the hairpin precursor overlapping, but not coincident with, the position of a known official mature mirna. The 9% of reads were aligned with mirna precursors regions with one mismatch. About 1% of alignments were found involving reads aligned with 2 3 -localized mismatches. Considering the multiple mapping issue, a fraction of considered reads mapped to more than one reference sequence. It is worth notice that identical mature mirnas belonging to different hairpins precursors were considered only once. Exactly matching reads did not show multiple mapping against different mirnas, but 27% of exactly matching reads aligned to more than one precursor. Also among the rare, but biologically meaningful, category of reads aligned to known mirnas precursors with 2 3 -localized mismatches, 33% matched multiple precursors. When focusing to the shorter/longer class of reads, 0.25% of reads aligned to 2 different precursors, whereas 2.5% and 24% of reads aligning with one mismatch, respectively matched more than one mirna and to more than one precursor. The above reported results and considerations clarify the need for the multiple mapping correction of read counts, which is explained in Methods. All results reported in the manuscript are based on corrected read counts. 1. Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R Dohm JC, Lottaz C, Borodina T, Himmelbauer H. Substantial biases in ultra-short read data sets from high-throughput DNA sequencing. Nucleic Acids Res. 2008;36(16):e

12 Table S1. Mapping of reads to mature mirnas and extended hairpin precursors. Reads matching to extended hairpin precursors in the region of mature mirnas sequences have been classified according to the match type and in respect to the known mirna position in category of read alignment and per number of reference sequences showing equally good alignments with the read. The table reports the number of reads mapping per mirna and per hairpin according to different types of alignments. The last seven columns show the percentages of reads, per alignment type, mapping to one or more distinct mirna/hairpin. Mature mirnas Total # % of total reads of all categories Exact mismatch '-mismatches Longer/Shorter Extended hairpin precursors Total # % of total reads of all categories Exact mismatch '-mismatches Longer/Shorter

13 Supplementary Figures and Tables Figure S2. Number of isomirs observed per mirna by considering those sequence variants with a read count of at least 10 reads or those accounting each for at least the 10% of the total count of the mirna. Figure S3. SET2 expression levels distributions for known and novel mirnas shown as boxplot with log10 scale. 13

14 Figure S4. Prevalence of 5' and 3' mirnas expression. A) Scatterplot showing 5' and 3' mirnas expression, both for the sister pairs including two known mirnas and for those including one new mirna. B) Distribution of log2ratio of 5' and 3' mirnas expression values. 14

15 Figure S5. A) The prevalence of 5 mornas is independent from what is the most expressed mirna. The plot shows the proportions of 5 and 3 arms from which is derived the most expressed morna, respectively to the arm of the most expressed mirna, for 82 hairpins expressing at least one mirna and one morna. B) Length ditributions for morna, known and new mirnas. 15

16 Figure S6. Relationship between mirna expression in SET2 cells and average sequence conservation in placental mammals. The horizontal dashed line in the main panel indicates an expression level of 10 4 ; the small panel shows only mirnas with SET2 expression over The extent of mirna conservation varies in a wide range, but highly expressed mirnas are always conserved. 16

17 Table S2. Expression values for 652 known mature mirnas sequenced in SET2 cells. Mature mirna Expression Mature mirna Expression Mature mirna Expression hsa-mir hsa-mir-148a hsa-mir-146b-5p hsa-mir hsa-mir-142-3p hsa-mir-19b hsa-mir hsa-mir-92a hsa-mir hsa-mir hsa-mir hsa-mir hsa-mir-181b hsa-mir-181a hsa-mir hsa-mir-126* hsa-mir-30e hsa-mir-99b hsa-let-7f hsa-mir hsa-mir-20a hsa-mir-103a hsa-mir hsa-mir hsa-mir hsa-mir-19a hsa-mir hsa-mir hsa-mir-23a hsa-mir-127-3p hsa-mir-199b-5p hsa-mir-106b hsa-mir-376c hsa-mir hsa-mir-30d hsa-mir-26a hsa-let-7a hsa-mir-142-5p hsa-mir-26b hsa-mir hsa-mir hsa-mir-125a-5p hsa-mir hsa-mir-320a hsa-mir-342-3p hsa-mir-342-3p hsa-mir-30b hsa-mir-18a hsa-mir hsa-mir-27a hsa-mir hsa-let-7i hsa-mir-151-3p hsa-let-7c hsa-mir hsa-mir-29a hsa-let-7b hsa-mir-423-3p hsa-mir-99a hsa-mir hsa-mir-151-5p hsa-mir hsa-let-7e hsa-mir hsa-mir-409-3p hsa-let-7g hsa-let-7g hsa-mir-140-3p hsa-mir hsa-mir-130a hsa-mir-21* hsa-mir-17* hsa-mir hsa-mir-127-5p hsa-mir-29b hsa-mir-125b hsa-mir-33a hsa-mir-181a* hsa-mir-487b hsa-mir-148b hsa-mir-30e* hsa-mir-532-5p hsa-mir-10a hsa-mir hsa-mir-146a hsa-mir-221* hsa-mir hsa-mir hsa-mir hsa-mir-486-5p hsa-mir-374b hsa-mir-769-5p hsa-mir-331-3p hsa-mir hsa-mir hsa-mir-181a-2* hsa-mir-27b hsa-mir-29c hsa-mir hsa-mir-369-3p hsa-mir-542-3p hsa-mir hsa-let-7d hsa-mir hsa-mir hsa-mir hsa-mir-136* hsa-mir-323b-3p hsa-mir-323b-3p hsa-mir-199a-3p hsa-mir hsa-mir-15b* hsa-mir-28-3p hsa-mir-148a* hsa-mir hsa-mir-324-5p hsa-mir hsa-mir-30a hsa-mir-15a hsa-mir-15b hsa-mir-301a hsa-mir-376b hsa-mir-106b* hsa-mir-223* hsa-mir hsa-mir-374a* hsa-mir-654-3p hsa-mir hsa-mir-323-3p hsa-mir-323-3p hsa-mir-485-3p 9959 hsa-mir-425* 9919 hsa-mir hsa-mir hsa-mir hsa-mir-18a* 9134 hsa-mir-99b* 8781 hsa-mir-378c 8710 hsa-mir-423-5p 8705 hsa-mir-144* 8686 hsa-mir hsa-mir-140-5p 8610 hsa-mir hsa-mir hsa-mir-335* 8244 hsa-mir hsa-mir-125b-2* 7774 hsa-mir hsa-mir-337-3p 7610 hsa-mir-92b 7514 hsa-mir-92b 7514 hsa-mir hsa-mir-27a* 7201 hsa-mir hsa-mir-199b-3p 7141 hsa-mir hsa-mir hsa-mir-181d 7116 hsa-mir-130b 6992 hsa-mir-493* 6910 hsa-mir-330-3p 6906 hsa-mir hsa-mir-542-5p 6563 hsa-mir hsa-mir-486-3p 6318 hsa-mir hsa-mir-361-5p 6084 hsa-mir hsa-mir hsa-mir hsa-mir-374a 5631 hsa-mir-374a 5631 hsa-mir-339-3p 5526 hsa-mir-301b 5513 hsa-mir-339-5p 5455 hsa-mir-500a* 5422 hsa-mir-193b

18 hsa-mir hsa-mir hsa-mir-24-2* 5183 hsa-mir hsa-mir-376a* 5159 hsa-mir hsa-mir-146b-3p 4950 hsa-mir-590-5p 4875 hsa-mir hsa-mir-20a* 4700 hsa-mir-377* 4684 hsa-mir hsa-mir-181c 4459 hsa-mir hsa-mir-193a-3p 4315 hsa-mir-193a-3p 4315 hsa-mir-34c-5p 4215 hsa-mir hsa-mir-29c* 4042 hsa-mir hsa-mir hsa-mir-20b 4004 hsa-mir hsa-mir-26a-2* 3930 hsa-mir hsa-mir hsa-mir hsa-mir-361-3p 3612 hsa-mir-92a-1* 3574 hsa-mir-296-5p 3488 hsa-mir-33b 3439 hsa-mir-1260b 3370 hsa-mir-19b-1* 3330 hsa-mir hsa-mir-362-5p 3246 hsa-mir-28-5p 3227 hsa-mir-28-5p 3227 hsa-mir hsa-mir-135a 2983 hsa-mir-125a-3p 2977 hsa-mir hsa-mir-23b 2901 hsa-mir hsa-mir hsa-mir-296-3p 2834 hsa-mir hsa-mir-103a-2* 2809 hsa-mir hsa-mir hsa-mir-769-3p 2689 hsa-mir-99a* 2669 hsa-mir-532-3p 2657 hsa-mir-10b 2635 hsa-mir-574-3p 2632 hsa-mir hsa-mir-501-3p 2581 hsa-mir-376a 2572 hsa-mir-376a 2572 hsa-mir-154* 2504 hsa-mir-324-3p 2459 hsa-mir-452* 2456 hsa-mir hsa-mir-130a* 2373 hsa-mir-299-5p 2353 hsa-mir-186* 2345 hsa-mir-224* 2331 hsa-mir hsa-mir-338-3p 2292 hsa-let-7d* 2229 hsa-mir-431* 2211 hsa-mir-16-2* 2196 hsa-mir-185* 2145 hsa-mir-106a 2131 hsa-mir-342-5p 2103 hsa-mir hsa-mir-501-5p 2093 hsa-mir-26b* 2063 hsa-mir hsa-mir hsa-mir hsa-mir-299-3p 1920 hsa-mir-450a 1916 hsa-mir-22* 1899 hsa-mir-29a* 1895 hsa-mir-222* 1881 hsa-mir-33a* 1845 hsa-mir-133a 1835 hsa-mir-487a 1828 hsa-mir hsa-mir hsa-mir hsa-let-7e* 1759 hsa-let-7a* 1737 hsa-mir-378* 1726 hsa-mir-132* 1721 hsa-mir-130b* 1712 hsa-mir-148b* 1632 hsa-mir hsa-mir-379* 1590 hsa-mir-379* 1590 hsa-mir-188-5p 1548 hsa-mir-129-5p 1538 hsa-mir hsa-mir-340* 1458 hsa-mir-7-1* 1383 hsa-mir-502-3p 1375 hsa-mir-181c* 1371 hsa-mir-654-5p 1358 hsa-mir-424* 1344 hsa-mir-129-3p 1329 hsa-mir-338-5p 1325 hsa-mir hsa-mir-485-5p 1276 hsa-mir-337-5p 1274 hsa-mir-409-5p 1254 hsa-mir-30d* 1249 hsa-mir-191* 1236 hsa-mir p 1236 hsa-mir-500a 1221 hsa-mir-500b 1221 hsa-mir-500b 1221 hsa-mir-93* 1212 hsa-mir hsa-mir-450b-5p 1180 hsa-mir-34b* 1176 hsa-mir p 1172 hsa-mir hsa-mir hsa-mir hsa-mir-9* 1144 hsa-let-7g* 1144 hsa-mir-590-3p 1124 hsa-mir hsa-mir hsa-mir-362-3p 1025 hsa-let-7i* 1022 hsa-mir hsa-mir hsa-mir-625* 988 hsa-mir hsa-mir-16-1* 956 hsa-mir-16-1* 956 hsa-mir-411* 942 hsa-mir-320b 924 hsa-mir-34b 909 hsa-mir-29b-2* 902 hsa-mir-30c-1* 866 hsa-mir-369-5p 840 hsa-mir-629* 810 hsa-mir-23a* 803 hsa-mir hsa-mir-192* 778 hsa-mir-330-5p 765 hsa-mir-30a* 749 hsa-mir hsa-mir-628-5p 714 hsa-mir-548j 708 hsa-mir-26a-1* 706 hsa-mir hsa-mir hsa-mir-200c 660 hsa-mir hsa-mir hsa-mir-432* 632 hsa-mir-671-3p 627 hsa-mir hsa-mir p 594 hsa-mir p 593 hsa-mir hsa-mir hsa-mir hsa-mir-380* 505 hsa-mir-150* 494 hsa-mir-101* 485 hsa-mir-32* 481 hsa-mir hsa-mir-27b* 470 hsa-mir hsa-mir hsa-mir hsa-mir-199a-5p 456 hsa-mir hsa-mir

19 hsa-mir hsa-mir hsa-mir-576-5p 397 hsa-mir-744* 397 hsa-mir-331-5p 390 hsa-mir-502-5p 386 hsa-let-7f-2* 383 hsa-mir-200b 376 hsa-mir p 373 hsa-mir-25* 371 hsa-mir-545* 369 hsa-let-7b* 367 hsa-mir-135b 367 hsa-mir hsa-mir hsa-mir-15a* 350 hsa-mir-33b* 346 hsa-mir-135a* 315 hsa-mir hsa-mir hsa-mir-454* 301 hsa-mir-454* 301 hsa-mir hsa-mir-18b 296 hsa-mir hsa-mir-671-5p 285 hsa-mir hsa-mir p 277 hsa-mir-19a* 273 hsa-mir-518c 269 hsa-mir-193a-5p 262 hsa-mir-30b* 255 hsa-mir-550a* 251 hsa-mir-509-3p 250 hsa-mir hsa-mir-541* 246 hsa-mir-92b* 246 hsa-mir-1268b 245 hsa-mir hsa-mir-34c-3p 240 hsa-mir hsa-mir hsa-mir hsa-mir p 235 hsa-mir-508-3p 232 hsa-mir-320c 229 hsa-mir hsa-mir-497* 216 hsa-mir hsa-mir-589* 203 hsa-mir hsa-mir hsa-mir hsa-mir-219-5p 193 hsa-mir hsa-mir-513a-5p 187 hsa-mir-10a* 185 hsa-mir hsa-mir p 183 hsa-mir hsa-mir-29b-1* 182 hsa-mir hsa-mir-145* 175 hsa-mir-145* 175 hsa-mir hsa-mir-548a-3p 174 hsa-mir hsa-mir-202* 171 hsa-mir-374b* 171 hsa-mir p 169 hsa-mir-200a 166 hsa-mir-195* 164 hsa-mir hsa-mir-143* 155 hsa-mir hsa-mir-767-5p 143 hsa-mir-194* 142 hsa-mir-2964a-3p 142 hsa-mir-550a 142 hsa-mir-517a 141 hsa-mir-517b 141 hsa-mir-20b* 140 hsa-mir hsa-mir hsa-mir hsa-mir-151b 133 hsa-mir-4662a-5p 132 hsa-mir-576-3p 132 hsa-mir hsa-mir-139-5p 128 hsa-mir-624* 119 hsa-mir-190b 118 hsa-mir p 115 hsa-mir-519a 115 hsa-mir-519d 114 hsa-mir hsa-mir p 112 hsa-mir-323-5p 110 hsa-mir-513a-3p 109 hsa-mir-518b 109 hsa-mir hsa-mir hsa-mir-378d 104 hsa-mir-513c 98 hsa-mir-548k 96 hsa-mir-548k 96 hsa-mir hsa-mir hsa-mir hsa-mir p 92 hsa-mir hsa-mir hsa-mir-24-1* 91 hsa-mir-642a 91 hsa-mir-664* 91 hsa-mir-548e 90 hsa-mir hsa-mir-520g 87 hsa-mir-30c-2* 86 hsa-mir hsa-mir-499-5p 84 hsa-mir hsa-mir-505* 82 hsa-mir p 81 hsa-mir hsa-mir hsa-mir hsa-mir hsa-mir hsa-mir hsa-mir-1255a 76 hsa-mir-196b 76 hsa-mir hsa-mir hsa-mir-877* 73 hsa-mir-2964a-5p 71 hsa-mir-146a* 69 hsa-mir p 68 hsa-mir-513b 67 hsa-mir-188-3p 66 hsa-mir-320d 64 hsa-mir hsa-mir-616* 61 hsa-mir p 60 hsa-mir hsa-mir p 59 hsa-mir p 59 hsa-mir p 57 hsa-mir hsa-mir hsa-mir-18b* 56 hsa-mir p 56 hsa-mir p 56 hsa-mir-548y 55 hsa-mir hsa-mir hsa-mir-183* 51 hsa-mir-548ab 50 hsa-mir hsa-mir-767-3p 47 hsa-mir hsa-mir p 46 hsa-mir-548d-3p 45 hsa-mir hsa-mir hsa-mir-548s 44 hsa-mir hsa-mir hsa-mir-518f 43 hsa-let-7a-2* 42 hsa-mir p 42 hsa-mir p 42 hsa-mir hsa-mir hsa-mir-106a* 40 hsa-mir hsa-mir p 40 hsa-mir hsa-mir hsa-mir-548b-3p 39 hsa-let-7f-1* 38 hsa-mir p 37 hsa-mir p 37 hsa-mir-92a-2* 37 hsa-mir p 36 hsa-mir

20 hsa-mir hsa-mir hsa-mir-141* 34 hsa-mir hsa-mir hsa-mir-196a 33 hsa-mir p 33 hsa-mir hsa-mir-520b 33 hsa-mir-520c-3p 33 hsa-mir-147b 32 hsa-mir-548l 32 hsa-mir-548o 32 hsa-mir hsa-mir-100* 30 hsa-mir-125b-1* 30 hsa-mir hsa-mir hsa-mir hsa-mir hsa-mir p 29 hsa-mir-133b 29 hsa-mir hsa-mir p 28 hsa-mir p 28 hsa-mir hsa-mir p 28 hsa-mir-520d-3p 28 hsa-mir hsa-mir-193b* 27 hsa-mir hsa-mir hsa-mir-517c 26 hsa-mir-770-5p 26 hsa-mir p 25 hsa-mir hsa-mir-524-5p 25 hsa-mir p 23 hsa-mir hsa-mir p 23 hsa-mir p 23 hsa-mir-556-3p 23 hsa-mir-708* 23 hsa-mir hsa-mir p 22 hsa-mir hsa-mir hsa-mir p 21 hsa-mir-323b-5p 21 hsa-mir hsa-mir p 21 hsa-mir-182* 20 hsa-mir-214* 20 hsa-mir p 20 hsa-mir hsa-mir p 20 hsa-mir-155* 19 hsa-mir hsa-mir hsa-mir p 19 hsa-mir-516a-5p 19 hsa-mir-520f 19 hsa-mir-548a-5p 19 hsa-mir hsa-mir hsa-mir-139-3p 18 hsa-mir-4662a-3p 18 hsa-mir p 18 hsa-mir hsa-mir-216a 17 hsa-mir p 17 hsa-mir-548n 17 hsa-mir-371-5p 16 hsa-mir hsa-mir p 15 hsa-mir hsa-mir hsa-mir p 15 hsa-mir-4659a-3p 15 hsa-mir-516b 15 hsa-mir hsa-mir hsa-mir hsa-mir hsa-mir-19b-2* 14 hsa-mir hsa-mir-526b* 14 hsa-mir-1226* 13 hsa-mir p 13 hsa-mir hsa-mir hsa-mir-483-3p 13 hsa-mir-891a 13 hsa-mir-208b 12 hsa-mir-23b* 12 hsa-mir hsa-mir-34a 12 hsa-mir p 12 hsa-mir p 12 hsa-mir hsa-mir-518d-3p 12 hsa-mir-551b 12 hsa-mir-582-5p 12 hsa-mir hsa-mir p 11 hsa-mir p 11 hsa-mir hsa-mir-378f 11 hsa-mir-449a 11 hsa-mir p 11 hsa-mir-483-5p 11 hsa-mir-518e 11 hsa-mir-519b-3p 11 hsa-mir-548u 11 hsa-mir-675* 11 hsa-mir hsa-mir hsa-mir hsa-mir-3150b-3p 10 hsa-mir p 10 hsa-mir-365* 10 hsa-mir hsa-mir p 10 hsa-mir p 10 hsa-mir-520h 10 hsa-mir-548ai 10 hsa-mir-548f 10 hsa-mir-628-3p 10 20

21 Table S3. KEGG pathways enriched in genes predicted to be target of one or more of the 21 mirnas most expressed in SET2. KEGG Pathway ID Genes Axon guidance hsa04360 EFNB2, SRGAP3, SEMA3G, SEMA6A, PLXNA2, SRGAP1, NCK2, GNAI2, DPYSL5, PLXNC1, NTN4, DPYSL2, SLIT1, NTNG1, ABLIM3, CXCL12, LIMK1, UNC5C, PLXNB2, EFNA3, PLXNA1, EPHB2, MET, GNAI3, SEMA6D, SRGAP2, ROCK1, KRAS, SEMA6B, CFL2, EPHA7, EPHA4, NRP1, SEMA4C, PAK1, UNC5D, MAPK1, RAC1, PAK4, CDC42, PAK6, EPHB3, SEMA4G, PPP3CA, EPHA8, RASA1, ABL1, SEMA5A, SEMA3A, EPHA5, UNC5A, EFNB3, NFAT5, PAK7, ROBO2, SEMA3B, RGS3, NTN1, SEMA3C, SEMA4F, EPHB4, EFNB1, PPP3CB 63 MAPK signaling pathway hsa04010 MAP4K3, TAOK3, MAP4K4, RPS6KA2, MAP2K3, FGF6, DUSP4, RPS6KA1, FOS, FGF4, NTRK2, TRAF6, NTF3, PDGFRA, EVI1, TGFBR1, STMN1, GADD45A, MRAS, MAP3K8, MAP3K4, MAP3K1, MAP3K13, IL1R1, PDGFB, PPM1B, SOS2, MAP3K7IP2, TNF, KRAS, FLNC, CACNB2, BDNF, DUSP5, MAPK11, DUSP9, FASLG, FGFR3, MAPT, CACNG4, DUSP2, CRKL, TAOK1, MAP3K10, STK4, IL1A, MAP2K4, RPS6KA4, SOS1, CACNA1D, DUSP16, PRKX, MAP3K3, RPS6KA5, PAK1, TP53, CRK, PLA2G10, MAPK1, FGF1, RAC1, SRF, CDC25B, DUSP8, CDC42, GRB2, CACNA2D4, FGF11, RAP1B, PPP3CA, AKT2, ACVR1B, PLA2G3, CACNA2D2, NLK, DUSP10, HSPA8, NF1, DUSP1, TGFBR2, FGF5, RAP1A, RASA1, MAPK14, ACVR1C, MAP3K12, MAP3K14, FAS, RPS6KA3, MAP3K5, PDGFRB, CASP3, PRKACB, AKT3, RAF1, PPP3CB 96 Colorectal cancer hsa05210 FZD7, AXIN2, APC2, BCL2, RALGDS, FOS, DVL3, PDGFRA, FZD5, TGFBR1, IGF1R, MET, MSH2, APPL1, SOS2, FZD8, KRAS, SMAD2, PIK3R1, SOS1, TP53, APC, MAPK1, RAC1, GRB2, AKT2, ACVR1B, FZD1, TGFBR2, PIK3R2, ACVR1C, FZD4, TCF7L1, PDGFRB, CASP3, PIK3CD, PIK3R3, FZD6, AKT3, FZD10, CCND1, SMAD4, RAF1 43 Renal cell carcinoma hsa05211 HIF1A, MET, SLC2A1, PDGFB, SOS2, KRAS, EGLN3, EPAS1, CRKL, PIK3R1, SOS1, VEGFA, PAK1, CRK, MAPK1, RAC1, PAK4, CDC42, CUL2, GRB2, PAK6, RAP1B, AKT2, TGFA, PIK3R2, RAP1A, ETS1, PAK7, ARNT2, GAB1, EGLN1, PIK3CD, PIK3R3, AKT3, RAF1 35 Oxidative phosphorylation hsa00190 ENSG , ATP6V1A, ATP6V1B2, ATP5G3, UQCRB 5 Chronic myeloid leukemia hsa05220 E2F3, GAB2, EVI1, E2F1, TGFBR1, CDKN1A, SOS2, KRAS, CDK6, CBL, E2F2, CRKL, PIK3R1, RB1, SOS1, TP53, CRK, MAPK1, RUNX1, GRB2, AKT2, ACVR1B, TGFBR2, PIK3R2, CBLB, ACVR1C, ABL1, BCR, PIK3CD, PIK3R3, BCL2L1, AKT3, CTBP2, CCND1, SMAD4, RAF1 36 TGF-beta signaling pathway hsa04350 E2F5, RBL2, TGFBR1, DOCK5, ID2, ID4, LTBP1, ROCK1, TNF, SMURF1, ZFYVE9, SMAD7, SMAD2, SMAD6, RBL1, SMAD5, BMP8B, ACVR1, ACVR2A, NOG, INHBB, GDF6, MAPK1, THBS1, THBS2, PPP2CA, SMAD1, BMP2, ACVR2B, ACVR1B, TGFBR2, BMPR1B, TFDP1, SP1, ACVR1C, ZFYVE16, BMPR2, PITX2, DCN, SMAD4 40 Focal adhesion hsa04510 ITGA3, BCL2, COL6A3, VASP, TLN2, COL4A6, PDGFRA, FN1, IGF1R, TNC, MET, VCL, COL5A1, ROCK1, CAV3, PDPK1, CAV2, PDGFB, COL1A1, ACTN1, SOS2, ITGB8, LAMA3, FLNC, FLT1, COL3A1, ITGA9, CRKL, ITGA2, ITGA6, PIK3R1, SOS1, VAV3, VEGFA, PAK1, CRK, MAPK1, THBS1, RAC1, PIP5K1C, PAK4, CDC42, GRB2, PAK6, VAV2, THBS2, RAP1B, COL5A2, ITGB3, AKT2, PIK3R2, ITGAV, COL1A2, ARHGAP5, RAP1A, ITGA11, CCND2, MYLK, COL2A1, LAMC1, IGF1, PAK7, PDGFRB, PIK3CD, PIK3R3, COL4A1, AKT3, CCND1, ITGA5, RAF1, LAMA4 Glioma hsa05214 CAMK2D, E2F3, PDGFRA, E2F1, IGF1R, CDKN1A, PDGFB, SOS2, KRAS, CDK6, FRAP1, CAMK2G, E2F2, PIK3R1, RB1, SOS1, CALM1,CALM2,CALM3, TP53, MAPK1, GRB2, AKT2, TGFA, PIK3R2, IGF1, PLCG1, PDGFRB, PIK3CD, PIK3R3, AKT3, CCND1, RAF1 Pancreatic cancer hsa05212 RALGDS, RALBP1, E2F3, STAT3, E2F1, TGFBR1, RALB, ARHGEF6, KRAS, CDK6, SMAD2, E2F2, PIK3R1, RB1, VEGFA, TP53, MAPK1, RAC1, CDC42, AKT2, ACVR1B, TGFA, TGFBR2, PIK3R2, JAK1, ACVR1C, PIK3CD, RALA, PIK3R3, BCL2L1, AKT3, CCND1, SMAD4, RAF1 Regulation of actin hsa04810 ITGA3, APC2, FGF6, MYH9, FGF4, TIAM1, F2R, LIMK1, PDGFRA, FN1, IQGAP2, FGD1, SSH2, MRAS, VCL, CHRM5, ROCK1, PPP1R12B, WASL, PDGFB, ACTN1, SOS2, ITGB8, ARHGEF6, KRAS, ITGA9,

22 cytoskeleton ARHGEF7, PIP4K2B, FGFR3, CRKL, ITGA2, CFL2, PIP4K2A, ITGA6, PIK3R1, SOS1, VAV3, BAIAP2, PAK1, APC, CRK, SSH1, MAPK1, FGF1, RAC1, PIP5K1C, DIAPH2, PAK4, CDC42, PIP4K2C, CHRM2, PAK6, VAV2, FGF11, ITGB3, PIK3R2, FGF5, ITGAV, ITGA11, GNA13, SLC9A1, ACTC1, MYLK, PIP5K3, MYH10, PAK7, PDGFRB, PIK3CD, PIK3R3, RDX, PFN2, ITGA5, RAF1, GIT1 Non-small cell lung cancer hsa05223 RARB, E2F3, FOXO3, E2F1, PDPK1, SOS2, KRAS, CDK6, RASSF1, E2F2, STK4, PIK3R1, RB1, SOS1, ENSG , TP53, MAPK1, GRB2, AKT2, TGFA, PIK3R2, PLCG1, PIK3CD, PIK3R3, AKT3, CCND1, RAF1 27 Calcium signaling pathway hsa04020 ADRA1B, CCKBR, CAMK2D, ITPKB, F2R, GRM1, ADRB2, SLC8A2, PDGFRA, EDNRB, ADRB3, ADCY1, ATP2B4, EDNRA, CHRM5, P2RX4, ERBB3, ATP2A2, CAMK2G, SLC8A1, CAMK4, PLCD1, ADCY7, ITPR1, PTK2B, ATP2B3, ATP2B2, ADCY2, PTGER3, ITPR3, CACNA1D, SPHK2, ATP2B1, CALM1,CALM2,CALM3, PRKX, P2RX1, SLC8A3, GRIN2A, ADCY9, CHRM2, SLC25A6, PPP3CA, PHKA1, ADRA1D, ADRB1, GRM5, HTR4, PLCB1, MYLK, ERBB4, ADCY3, GNAL, PLCG1, PDGFRB, PRKACB, GNAQ, DRD1, PPID, PPP3CB 59 Dentatorubropallid oluysian atrophy (DRPLA) hsa05050 MAGI2, WWP2, CASP7, BAIAP2, WWP1, RERE, INSR, ITCH, CASP3, MAGI1 10 Fc epsilon RI signaling pathway hsa04664 MAP2K3, PRKCD, GAB2, SOS2, BTK, TNF, KRAS, MAPK11, PRKCE, LYN, PDK1, PIK3R1, MAP2K4, IL13, SOS1, VAV3, PLA2G10, MAPK1, RAC1, GRB2, VAV2, AKT2, PLA2G3, PIK3R2, MAPK14, PLCG1, PIK3CD, PIK3R3, AKT3, MS4A2, SPNS1, RAF1 32 Prostate cancer hsa05215 CCNE2, BCL2, E2F3, PDGFRA, E2F1, IGF1R, CDKN1A, PDPK1, PDGFB, SOS2, KRAS, FRAP1, E2F2, PIK3R1, RB1, SOS1, HSP90B1, FOXO1, TP53, MAPK1, GRB2, CREB5, AKT2, TGFA, PIK3R2, TCF7L1, IGF1, CREB1, AR, PDGFRB, PIK3CD, PIK3R3, AKT3, CCND1, RAF1, CREB3L2 Melanogenesis hsa04916 FZD7, CAMK2D, GNAI2, DVL3, WNT3, FZD5, EDNRB, GNAI3, ADCY1, FZD8, KRAS, EDN1, KIT, CAMK2G, ADCY7, ADCY2, CALM1,CALM2,CALM3, PRKX, MITF, MAPK1, KITLG, WNT1, ADCY9, WNT10B, WNT7B, FZD1, PLCB1, FZD4, TCF7L1, ADCY3, CREB1, PRKACB, WNT10A, GNAQ, FZD6, FZD10, RAF1, ADCY6, CREB3L mtor signaling pathway hsa04150 RPS6KA2, RPS6KA1, HIF1A, TSC1, PDPK1, RHEB, FRAP1, ULK1, PRKAA2, PIK3R1, VEGFA, MAPK1, PRKAA1, ULK2, AKT2, PIK3R2, DDIT4, RPS6KA3, IGF1, PIK3CD, PIK3R3, AKT3 22 ECM-receptor interaction hsa04512 ITGA3, COL6A3, SDC2, COL4A6, FN1, TNC, SDC4, COL5A1, COL1A1, ITGB8, LAMA3, COL3A1, ITGA9, SDC1, DAG1, ITGA2, ITGA6, SV2A, SV2B, FNDC3A, THBS1, THBS2, COL5A2, ITGB3, ITGAV, COL1A2, ITGA11, COL2A1, LAMC1, COL4A1, ITGA5, LAMA4 32 ErbB signaling pathway hsa04012 CAMK2D, NCK2, CDKN1A, ERBB3, SOS2, KRAS, CBL, FRAP1, CAMK2G, CRKL, PIK3R1, MAP2K4, SOS1, PAK1, CRK, MAPK1, ABL2, PAK4, GRB2, PAK6, AKT2, TGFA, PIK3R2, CBLB, ABL1, ERBB4, PAK7, PLCG1, GAB1, PIK3CD, PIK3R3, AKT3, EREG, RAF1 34 GnRH signaling pathway hsa04912 CAMK2D, MAP2K3, PRKCD, ADCY1, MAP3K4, MAP3K1, SOS2, KRAS, MAPK11, CAMK2G, ADCY7, ITPR1, PTK2B, ADCY2, MAP2K4, SOS1, ITPR3, CACNA1D, CALM1,CALM2,CALM3, PRKX, MAP3K3, PLA2G10, MAPK1, MMP14, CDC42, GRB2, ADCY9, PLA2G3, MAPK14, PLCB1, MMP2, ADCY3, PRKACB, GNAQ, RAF1, ADCY6 36 Melanoma hsa05218 FGF6, E2F3, FGF4, PDGFRA, E2F1, IGF1R, CDKN1A, MET, PDGFB, KRAS, CDK6, E2F2, PIK3R1, RB1, TP53, MITF, MAPK1, FGF1, FGF11, AKT2, PIK3R2, FGF5, IGF1, PDGFRB, PIK3CD, PIK3R3, AKT3, CCND1, RAF1 29 Amyotrophic lateral sclerosis (ALS) hsa05030 BCL2, CAT, NEFL, NEFH, TP53, RAC1, PPP3CA, RAB5A, NEFM, SLC1A2, BCL2L1 11 Small cell lung cancer hsa05222 ITGA3, CCNE2, RARB, BCL2, E2F3, TRAF6, COL4A6, E2F1, FN1, LAMA3, CDK6, E2F2, PTGS2, ITGA2, ITGA6, PIK3R1, RB1, ENSG , TP53, TRAF3, APAF1, AKT2, PIK3R2, ITGAV, PIAS3, LAMC1, PIK3CD, PIK3R3, BCL2L1, COL4A1, AKT3, CCND1, LAMA4 33 Long-term hsa04720 RPS6KA2, CAMK2D, RPS6KA1, GRIA1, GRM1, ADCY1, KRAS, CAMK2G, CAMK4, ITPR1, ITPR3, CALM1,CALM2,CALM3, PRKX, MAPK1, GRIN2A, GRIA2, RAP1B, PPP3CA, RAP1A, GRM5, PLCB1, 26 22

23 potentiation RPS6KA3, PRKACB, GNAQ, RAF1, PPP3CB Wnt signaling pathway hsa04310 FZD7, CTNNBIP1, AXIN2, APC2, CAMK2D, NKD1, DVL3, WNT3, FZD5, DOCK5, LRP6, TBL1X, ROCK1, FZD8, VANGL2, SMAD2, CAMK2G, FOSL1, PRKX, TP53, APC, DAAM1, VANGL1, WNT1, RAC1, FBXW11, WNT10B, PPP2CA, WNT7B, PPP3CA, PRICKLE1, NLK, PSEN1, FZD1, PPARD, PLCB1, CCND2, FZD4, SENP2, NFAT5, TCF7L1, PRICKLE2, PRKACB, WNT10A, FZD6, CTBP2, FZD10, CCND1, SMAD4, PPP3CB 50 T cell receptor signaling pathway hsa04660 CD4, NCK2, FOS, MAP3K8, ICOS, SOS2, TNF, KRAS, CBL, PDK1, PIK3R1, CARD11, SOS1, VAV3, PAK1, IL10, PAK4, CDC42, GRB2, PAK6, VAV2, PPP3CA, AKT2, PIK3R2, CBLB, MAP3K14, NFAT5, PAK7, PLCG1, PIK3CD, PIK3R3, AKT3, SPNS1, PPP3CB 34 Adipocytokine signaling pathway hsa04920 PPARGC1A, IRS2, PRKAG2, STAT3, ACSL1, CAMKK1, SLC2A4, SLC2A1, ACSL4, TNF, LEPR, FRAP1, TNFRSF1B, PRKAA2, ACSL3, ADIPOR2, CAMKK2, ENSG , ACSL6, IRS1, PRKAA1, SOCS3, AKT2, JAK1, PRKAB2, AKT3, JAK2, PPARA 28 Type II diabetes mellitus hsa04930 IRS2, SOCS1, PRKCD, SLC2A4, SOCS4, TNF, FRAP1, PRKCE, PIK3R1, CACNA1D, SOCS2, MAPK1, IRS1, INSR, SOCS3, PIK3R2, PIK3CD, PIK3R3 18 Circadian rhythm hsa04710 CRY2, BHLHB2, PER3, BHLHB3, PER2, NPAS2, ARNTL, CLOCK 8 Insulin signaling pathway hsa04910 PPARGC1A, IRS2, PRKAG2, SOCS1, PPP1R3B, TSC1, PFKP, SLC2A4, SOCS4, PDPK1, SOS2, KRAS, RHEB, PRKAR2B, CBL, FRAP1, PRKAA2, CRKL, PIK3R1, FLOT2, SOS1, CALM1,CALM2,CALM3, PRKX, SOCS2, FOXO1, CRK, MAPK1, IRS1, INSR, PRKAA1, PDE3B, GRB2, SOCS3, AKT2, PHKA1, PIK3R2, CBLB, PRKAB2, PRKAR1A, TRIP10, PRKACB, PIK3CD, PIK3R3, PPP1R3D, AKT3, RAF1 46 Bladder cancer hsa05219 E2F3, E2F1, CDKN1A, DAPK1, KRAS, IL8, RASSF1, E2F2, FGFR3, RB1, VEGFA, RPS6KA5, TP53, MAPK1, THBS1, MMP2, CCND1, RAF1 18 Ubiquitin mediated proteolysis hsa04120 UBE2G2, UBE2D1, CDC27, RNF7, SOCS1, UBE2D2, UBE2G1, TRAF6, HERC2, UBE2Z, UBE3C, UBE2R2, HERC3, MAP3K1, BIRC6, UBE2J1, CUL3, UBE2I, SMURF1, WWP2, UBE2W, CBL, MID1, UBE2B, FBXW7, TRIM37, WWP1, CDC34, FBXW11, CUL2, SOCS3, CBLB, NEDD4L, PIAS3, CDC23, UBE2O, CUL5, NEDD4, UBE2F, ITCH, UBE2D3, CUL4B, UBE2A 43 Tryptophan metabolism hsa00380 CAT, WARS2, CARM1, NFX1, ALDH1A3 5 Basal cell carcinoma hsa05217 FZD7, AXIN2, APC2, DVL3, WNT3, FZD5, FZD8, TP53, APC, WNT1, PTCH1, WNT10B, WNT7B, BMP2, FZD1, GLI2, FZD4, TCF7L1, WNT10A, FZD6, FZD10 21 Phosphatidylinosit ol signaling system hsa04070 SYNJ1, ITPKB, OCRL, PIK3C2B, INPP5A, DGKG, DGKD, PLCD1, PIP4K2B, ITPR1, PIP4K2A, PIK3R1, ITPR3, CALM1,CALM2,CALM3, IMPA2, PIP5K1C, PIP4K2C, ITPK1, PIK3C2A, PIK3R2, PLCB1, PIP5K3, PLCG1, PIK3CD, DGKZ, PIK3R3 26 Dorso-ventral axis formation hsa04320 FMN2, NOTCH2, SPIRE1, SOS2, KRAS, ETV6, SOS1, MAPK1, GRB2, ERBB4, ETS1, RAF1 12 Jak-STAT signaling pathway hsa04630 PRLR, SOCS1, STAT2, OSM, STAT3, CNTFR, LIFR, SOCS4, SOCS5, SOS2, SPRED2, LEPR, CBL, STAM, STAT6, STAM2, PIK3R1, SPRY3, IL13, SOS1, IFNAR2, IL2RA, SOCS2, OSMR, GHR, SPRY4, IL12A, IL10, GRB2, SOCS3, AKT2, PIK3R2, CBLB, LIF, JAK1, PIAS3, CCND2, SPRY1, IL28RA, SPRED1, CLCF1, PIK3CD, PIK3R3, BCL2L1, AKT3, CCND1, JAK2, SPRY2 48 Adherens junction hsa04520 PTPRM, TGFBR1, IGF1R, MET, VCL, SNAI1, WASF3, WASL, ACTN1, YES1, SMAD2, BAIAP2, MAPK1, PTPRJ, RAC1, INSR, CDC42, MLLT4, ACVR1B, SSX2IP, NLK, TGFBR2, PVRL1, ACVR1C, TCF7L1, SMAD4 Endometrial cancer hsa05213 AXIN2, APC2, FOXO3, PDPK1, SOS2, KRAS, PIK3R1, SOS1, TP53, APC, MAPK1, GRB2, AKT2, PIK3R2, TCF7L1, PIK3CD, PIK3R3, AKT3, CCND1, RAF Pyrimidine metabolism Complement and coagulation hsa00240 NT5E, NME4, CANT1, RRM2, POLA1, AK3, NME7, POLR2D, POLR3G, ENTPD6, NME6 11 hsa04610 F2R, FGA, CR2, THBD, F3, MASP1, SERPINE1, PLAU 8 23

24 cascades Neurodegenerative Diseases hsa01510 BCL2, NR4A2, NEFH, PRNP, CASP7, MAPT, APP, LRRK2, FBXW7, GRB2, SETX, PSEN1, CASP3, BCL2L1, HSPA5 15 Acute myeloid leukemia hsa05221 STAT3, SOS2, KRAS, KIT, FRAP1, PIK3R1, SOS1, MAPK1, RUNX1, GRB2, AKT2, PIK3R2, PPARD, TCF7L1, RUNX1T1, PIK3CD, PIK3R3, AKT3, CCND1, RAF1 20 Autoimmune thyroid disease hsa05320 CD86, FASLG, CD80, IL10, FAS 5 24

25 TABLE S4. Functional insights concerning the 21 most expressed mirnas in SET2 cells by literature search. hsa-mir Chr. location Inter/intragenic Expression Cumulative % of total known mirnas expression Role in normal and/or abnormal hematopoiesis mir-21 17q23.1 inter commonly overexpressed in solid tumors of the lung, breast, stomach, prostate, colon, brain, head and neck, esophagus and pancreas. Expression of mir-21 has been found to be deregulated in almost all types of cancers and therefore was classified as an oncomir. 1 Myeloma cell adhesion to bone marrow stromal cells confers drug resistance by microrna-21 upregulation 2 MiR-21 additionally regulates various immunological and developmental processes. 1 down-regulation of mir-21, mir-23a and mir-503 aggravated apoptosis of bone marrow mesenchymal stem cell; over-expression of mir-21, mir-23a and mir-210 could promote the survival of MSCs exposed to hypoxia/sd. 3 up-regulation of mir-21 by type I collagen. 4 cellular apoptosis was elevated and cell proliferation was decreased in mice deficient of mir-21. a large number of validated or predicted mir-21 target genes were up-regulated in mir-21-null cells - up-regulation of Spry1, Pten, and Pdcd4 when mir-21 was ablated coincided with reduced phosphorylation of ERK, AKT, and JNK. 5 Silencing of mir-21 reversed the activated phenotype of T cells from patients with SLE-namely, enhanced proliferation, interleukin 10 production, CD40L expression and their capacity to drive B cell maturation into Ig-secreting CD19+CD38(hi)IgD-(plasma cells). 6 induced angiogenesis through AKT and ERK activation and HIF-1α expression. 7 involved in NF-κB signaling 8 and TLR signaling. Involvement of mir-21 in resistance to daunorubicin by regulating PTEN expression in the leukaemia K562 cell line. 9 MicroRNA-21 expression in CD4+ T cells is regulated by STAT3. 10 overexpression of mir-21 leads to a pre-b malignant lymphoid-like phenotype. 11 Increased mir-21 expression during human monocyte differentiation into dendritic cells. 12 higher expression in non-responder LLC-tretaed patients 13 and it is overespressed in del 17p- LLc patients 14. p53/p73/p63 appears to regulate the processing of mir mirna-21 regulates arsenic-induced anti-leukemia activity in HL60 and K562 cell lines. 16 MiR-21 regulates adipogenic differentiation through the modulation of TGF-beta signaling in 25

26 mesenchymal stem cells derived from human adipose tissue. 17 Gfi1 regulates mir-21 and mir-196b to control myelopoiesis. 18 MicroRNA-21 stimulate MAP kinase signalling in fibroblasts. 19 underexpressed in osteo-differentiated multipotent mesenchymal stromal cells. 20 MicroRNA-148/152 impair innate response and antigen presentation of TLR-triggered dendritic cells by targeting CaMKIIα. 21 targets DNMT3B in AML. 22 mir-148a 7p15.2 inter higher expression in non-responder LLC-tretaed patients 13 and it is overespressed in del 17p- LLc patients 14. mir-148a expression was suppressed in HSCs and its level inversely correlated with the previously verified target, DNA methyltransferase 3B, suggesting dependence of de novo DNA methylation in HSCs on mir-148a. 23 mir-146b-5p 10q24.32 inter Increased accumulation of mir-146b was observed in cellular stage PMF (p = ) but not ET megakaryopoiesis. 24 Inter mir-101-1: 1p31.3 mir-101 mir-101-2: 9q24.1 Intra (RCL1) Targets MAPK Phosphatase-1 regulating activation of MAPKs in macrophages 25 Negatively regulator of EZH2 expression Up-regulated during megakaryocytopoiesis 31 mir-142-3p 17q22 inter mir-19b mir-19b-1: 13q31.3 mir-19b-2: Xq26.2 Inter (MIR17HG) inter Preferentially expressed in hematopoietic tissue 32 Regulated expression in HSC, and negative regulator of CD Highly expressed in normal leukocytes Over expressed in pre-b-all 34 Down-regulated during Imatinib therapy in CML. 35 decreased expression in AML. 36 Included in the mir-17~92 cluster Upregulated in CML BC 37 capable of promoting T-ALL development in a mouse model. 38 mir17-92 cluster is a direct transcriptional target of c-myc; mir-19b are absolutely required and largely sufficient to recapitulate the oncogenic properties in myc-induced-b-cell-lymphonma. 39 Pleiotropic anti-myeloma activity of ITF2357: inhibition of interleukin-6 receptor signaling and repression of mir-19a and mir-19b