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Category : Protein Folding

Scientists Pinpoint the Spots of Early Prion Protein Deposition in the Retina – Gilmore Health News

What is prion disease?

Prion diseases are a type of neurodegenerative disorder that is produced by the accumulation of abnormal proteins in the brain. Prion disease predominantly affects the brain, but it can also attack the eyes, especially the light-sensitive photoreceptors called cones and rods which are present in the retina, and other organs. These are steadily deteriorating and typically deadly diseases of the brain and can occur in people as well as some other mammals. Examples: mad cow disease in cattle, Creutzfeldt-Jakob disease in people, chronic wasting disease in deer, elk, and moose, and bovine spongiform encephalopathy in cattle.

Prion Infected Retina. Image Courtesy of NIH

Read Also: Creutzfeldt-Jakob Disease: A Lab Technician Gets Disease 7 Years After Accidental Cut

A recent study done by scientists at the National Institutes of Health states that the initial eye injury from prion disease occurs in the cone photoreceptor cells, especially in the cilia and the ribbon junctions. The researchers say, their discovery may provide understanding on human retinitis pigmentosa, an inherited disorder with closely related photoreceptor degradation advancing into blindness. The understanding of how prion diseases develop in the eyes can aid scientists to look for strategies to steady the growth of prion diseases.

In their study, the researchers, from NIHs National Institute of Allergy and Infectious Diseases at Rocky Mountain Laboratories in Hamilton, Montana, used research mice diseased with scrapie, a prion disease routine to sheep and goats. Scrapie is nearly associated with human prion diseases, Creutzfeldt-Jakob disease (CJD).

Read Also: Alzheimers: What If It Is Similar to Mad Cow Disease?

The scientists discovered the accumulation of a lump of prion protein was seen first in cone photoreceptors next to the cilia, pipe-like formation needed for transferring molecules between cellular sections with help of the confocal microscope. The study suggests that by obstructing the movement through cilia, these clumps may layout a key early process by which prion infection particularly smashes photoreceptors. Relatable findings were seen in rods as well.Exactly before the destruction of ribbon synapses (specialized neutron links present in the eye and ear neural pathways) and end of photoreceptors, there was an accumulation of prion protein in these structures.

The findings from this study were unique and were never observed before. The association between prion protein and retinal injury is probably present in all prion-vulnerable species, as well as humans.

There are other kinds of declining disorders that are also distinguished by abnormal folding of self-proteins, such as Alzheimers and Parkinsons diseases. The scientists are looking to investigate if related findings take place in the retinas of these people.

Read Also: An Artificial Retina to Restore Sight Could Soon Become a Reality

Prion-induced photoreceptor degeneration begins with misfolded prion protein accumulation in cones at two distinct sites: cilia and ribbon synapses

Prion Seeds Distribute throughout the Eyes of Sporadic Creutzfeldt-Jakob Disease Patients

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Scientists Pinpoint the Spots of Early Prion Protein Deposition in the Retina - Gilmore Health News

Recommendation and review posted by Alexandra Lee Anderson

Targeting oncoproteins with a positive selection assay for protein degraders – Science Advances

RESULTS AND DISCUSSION

To develop a positive selection assay for protein degraders, we made a bicistronic lentivirus encoding (i) a POI fused to a modified version of deoxycytidine kinase (hereafter called DCK*) that converts the non-natural nucleoside 2-bromovinyldeoxyuridine (BVdU) into a poison (4) and (ii) green fluorescent protein (GFP). We reasoned that GFP could be used to mark reporter-positive cells, to FACS (fluorescence-activated cell sorting) sort for cells with the desired reporter mRNA levels, and to count cells in multiwell plate assays. In our initial proof-of-concept experiments, we used this virus to create 293FT cells expressing the IMiD target IKZF1 (1, 2, 5) fused to DCK* and compared them to cells expressing unfused DCK* or unfused IKZF1 (Fig. 1, A and B). As expected, the IMiD pomalidomide (POM) down-regulated DCK*-IKFZ1 and IKZF1 but not DCK* (Fig. 1B). We also confirmed that 293FT cells expressing DCK*-IKZF1 or unfused DCK* were more sensitive to BVdU than 293FT cells expressing IKZF1 alone or infected with an empty vector (EV) (Fig. 1C). The increased BVdU sensitivity of the DCK* cells relative to the DCK*-IKZF1 cells is likely explained by the higher protein levels of DCK* compared to DCK*-IKZF1 (Fig. 1B). Similar results were observed with cells expressing DCK*-K-Ras (G12V), DCK*-Cyclin D1, DCK*-FOXP3, and DCK*-MYC, indicating that DCK* remains active when fused to a variety of proteins (fig. S1). POM increased the BVdU median effective concentration (EC50) of cells expressing DCK*-IKZF1 but not of cells expressing DCK* (Fig. 1D).

(A) Vector schematic. DCK*, variant deoxycytidine kinase with Ser74Glu, Arg104Met, and Asp133Ala substitutions; V5, V5 epitope tag; GGS, Gly-Gly-Ser spacer; IRES, internal ribosomal entry site. (B) Immunoblot analysis of 293FT cells infected with the lentiviral vectors depicted in (A) and then treated with 1 or 10 M POM, as indicated by the triangles, for 24 hours. (C and D) Relative survival of 293FT cells infected with the lentiviral vectors depicted in (A) and then treated with the indicated concentrations of BVdU for 4 days. In (D), cells were also treated with 1 M (POM) starting 24 hours before BVdU was added. n = 3 biological replicates. (E and F) Number of GFP-positive 293FT cells infected to produce DCK* (E) or DCK*-IKZF1 (F) using the vectors in depicted in (A) and then treated with indicated concentrations of POM and BVdU in 384-well plate format. POM was added 24 hours before treatment with BVdU for 4 days. n = 4 biological replicates. (G) Immunoblot analyses of cells treated as in (E) and (F). (H) Fluorescence data of 384-well plate containing 293FT cells expressing DCK*-IKZF1 treated with DMSO (columns 1 to 11 and 24) or 1 M POM (columns 12 to 23), followed 24 hours later by the addition of 100 M BVdU for 4 days (columns 1 to 24).

Next, we seeded either the DCK*-IKZF1 cells or DCK* cells in 384-well plates and treated the wells with increasing amounts of POM or with dimethyl sulfoxide (DMSO). We added BVdU 24 hours later and measured cell viability 4 days thereafter by measuring the number of GFP-positive objects per well. POM again promoted the survival of the DCK*-IKZF1 cells, but not the DCK* cells, over a range of POM and BVdU concentrations (Fig. 1, E to G). In anticipation of using this assay for a high-throughput screen, we next seeded the DCK*-IKZF1 cells in 384-well plates and treated half the wells with POM and half the wells with DMSO, followed 24 hours later by BVdU (Fig. 1H). Measuring GFP-positive objects 4 days later produced a favorable Z value (0.7) for this assay.

Encouraged by these findings, we did a pilot screen with 293FT cells expressing DCK*-IKZF1 or unfused DCK* grown in 384-well plates and a library of ~2000 bioactive compounds, which included lenalidomide (LEN) and POM (Fig. 2, A to C). Each well received a different compound at a concentration of approximately 10 M by pin transfer, followed the next day by BVdU. BVdU was added at 100 M to the DCK*-IKZF1 cells and at 10 M to the DCK* cells to achieve comparable cell killing despite the higher levels of DCK* relative to DCK*-IKZF1 (fig. S2). Four days thereafter, the GFP fluorescence for each well was measured and converted to a z score based on the GFP fluorescence values for the other wells on its plate. LEN and POM scored positively (z > 2) in the DCK*-IKZF1 screen but not the DCK* screen (Fig. 2, B to E). Some compounds promoted the survival of both DCK* cells and the DCK*-IKZF1 cells, including compounds that interfere with BVdU uptake (e.g., dipyridamole) (6, 7) or incorporation into DNA (e.g., thymidine) (compare Fig. 2, B and C). Such assay positives could be largely eliminated by subtracting the DCK* z score for each chemical from its DCK*-IKZF1 z score (Fig. 2F). For comparative purposes, we also did a screen with the same 2000 bioactive compound collection using 293FT cells expressing a bicistronic mRNA encoding (i) an IKZF1Firefly luciferase (Fluc) fusion and (ii) Renilla luciferase (Rluc), using a decrease in the Fluc/Rluc ratio to identify IKZF1 degraders (Fig. 2, G to I, and fig. S3) (2). As expected for such a down assay, this screen underperformed the DCK*-IKZF1 up screen with respect to both signal to noise and the number of false positives, which included compounds that inhibit Cap-dependent translation (e.g., VX-11e or BIX02565) (810). Compounds that nonselectively inhibit transcription, translation, or protein folding would predictably be especially problematic for Fluc fusions with shorter half-lives than the Rluc internal control. Notably, the transcriptional inhibitor actinomycin D and the translational inhibitor cycloheximide did not promote the survival of the DCK*-IKZF1 cells at any concentration tested (fig. S4).

(A) Scheme for positive selection protein degradation assay. (B and C) Representative fluorescence data of 384-well plates containing 293FT cells expressing DCK* (B) or DCK*-IKZF1 (C) treated with compounds in the Selleck BioActive Library (one compound per well), followed 24 hours later by the addition of BVdU at the EC85 (10 and 100 M, respectively) for 4 days. BVdU was omitted in column 1. Columns 23 and 24 contained 10 M POM and 12.5 M dipyridamole (DiP), respectively. Library wells containing POM and DiP are indicated by the red and white arrows, respectively. (D and E) Z-distribution of GFP fluorescence of DCK* cells (D) and DCK*-IKZF1 cells (E) screened with the full Selleck BioActive Library. LEN and POM are indicted by the blue circle and red triangle, respectively. n = 2 biological replicates. (F) Corrected z scores obtained by subtracting z scores in (D) from z scores in (E). (G) Scheme for negative selection screening using the dual-luciferase reporter assay. (H and I) Z scores of Fluc/Rluc ratio of 293FT IKZF1-Fluc-IRES-Rluc cells after screening with the Selleck BioActive Library for 8 hours (H) or 4 days (I). n = 2 biological replicates.

As one way to minimize false positives, we seeded 384-well plates with a 1:1 mixture of 293FT cells expressing either (i) DCK*-IKZF1 and GFP or (ii) DCK* and TdTomato (Fig. 3A). Both POM and dipyridamole increased the number of GFP-positive cells, but dipyridamole was readily identified as a false positive by examining the TdTomato fluorescence channel (Fig. 3B). We then repeated these experiments in 384-well plate format, exposing the cells to 10 different concentrations of a small library of approximately 100 analogs of POM that we had synthesized, which included the known IKZF1 degraders LEN, POM, and avadomide (MI-2-65) (11) and several uncharacterized IMiD-like molecules from the literature (12) (Fig. 3C and tables S1 and S2). This library was generated to test whether our assay could correctly identify the known IKZF1 degraders and identify additional IKZF1 degraders made by alternative diversification of the aryl moiety of POM. LEN, POM, and avadomide all scored in our assay (Fig. 3C). In addition, several previously uncharacterized compounds, including MI-2-61 and MI-2-197, appeared to be at least as potent as POM in this screen and in confirmatory immunoblot assays (Fig. 3, C to F, and fig. S5). Our screen also correctly classified compounds that did not down-regulate IKZF1 in immunoblot assays, including some (e.g., MI-2-192 and MI-2-118) that still bound to cereblon in biochemical assays (fig. S5).

(A) Scheme for in-well GFP/TdTomato competition assay. 293FT cells were infected to produce DCK*-IKZF1 and GFP or DCK* and TdTomato using bicistronic vectors analogous to those depicted in Fig. 1A. (B) Top: Heatmap of the fold change (relative to treatment with DMSO) of GFP fluorescence of a 1:1 mixture of GFP-positive DCK*-IKZF1 and TdTomato-positive DCK* cells treated with 3.125, 6.25, 12.5, or 25 M POM or dipyridamole or with vehicle (DMSO) and followed 1 day later by the addition of 100 M BVdU for 4 days. Bottom: Heatmap of the fold change (relative to treatment with DMSO) of the ratio of GFP fluorescence to TdTomato fluorescence of the cells treated in (A). n = 2 biological replicates. (C) Heatmap of the fold change (relative to treatment with DMSO) of the ratio of GFP to TdTomato fluorescence of a 1:1 mixture of GFP-positive DCK*-IKZF1 and TdTomato-positive DCK* cells treated with 1.3 nM, 3.8 nM, 11.4 nM, 34 nM, 102 nM, 310 nM, 920 nM, 2.78 M, 8.33 M, and 25 M of the indicated IMiDs, as indicated by the triangles, or with vehicle (DMSO), and followed 1 day later by the addition of 100 M BVdU for 4 days. n = 2 biological replicates. (D) Immunoblot analysis of 293FT cells lentivirally transduced to express IKZF1-V5 and treated with the indicated IMiD derivatives for 24 hours using the same concentration range as in (C). (E) Structures of POM and IMiD MI-2-61. (F) Quantification of immunoblot data in (D); n = 2 biological replicates.

To begin looking for non-IMiD IKZF1 degraders, we screened ~546 metabolic inhibitors and anticancer drugs at 10 different concentrations using the DCK*-IKZF1 293FT cells in 384-well plate format (tables S3 and S4) (13). In parallel, we counterscreened against unfused DCK* cells. Spautin-1 (14), like POM, promoted the survival of the DCK*-IKZF1 cells, but not the DCK* cells, in a dose-dependent manner (Fig. 4, A to D). We confirmed that Spautin-1 down-regulated DCK*-IKZF1 and V5-tagged exogenous IKZF1 but not DCK* (Fig. 4E). IKZF1-V5 was among the 100 most down-regulated proteins after 24 hours of Spautin-1 treatment, as determined by quantitative mass spectrometry proteomics (fig. S6 and table S5). Until the direct target of Spautin-1 linked to IKZF1 turnover is known, it is impossible to know how many of these changes in protein abundance are direct versus indirect and on-target versus off-target. Notably, Spautin-1, unlike POM, down-regulated IKZF1 in cells lacking cereblon (Fig. 4F).

(A) Chemical structure of Spautin-1. (B) GFP fluorescence of DCK*-IKZF1 and DCK* 293FT cells treated with ranolazine, Spautin-1, and resveratrol at concentrations of 25 M, 8.33 M, 2.78 M, 920 nM, 310 nM, 102 nM, 34 nM, 11.4 nM, 3.8 nM, and 1.3 nM, as indicated by the triangle, followed 24 hours later by the addition of BVdU at the EC85. Shown for comparison are cells treated with POM (10 M) or dipyridamole (DiP) (12.5 M) before adding BVdU. n = 2 biological replicates. (C and D) Quantification of GFP fluorescence from (B) for Spautin-1 (C) and for an analogous titration with POM (D). (E) Immunoblot analysis of 293FT cells infected with lentiviruses as in Fig. 1A and treated with the indicated concentrations of Spautin-1 for 24 hours. (F) Immunoblot analysis of isogenic 293FT CRBN +/+ and CRBN / cells transduced to express IKZF1-V5 and treated with the indicated concentrations of Spautin-1 or POM (1 M) for 24 hours. (G) Immunoblot analysis of 293FT cells stably expressing IKZF1-V5 and simultaneously treated with MLN7243 (1 M), MLN4924 (1 M), MG132 (1 M), Spautin-1 (10 M), or POM (1 M) for 24 hours as indicated. (H and I) Immunoblot (H) and RT-qPCR (I) analysis of KMS11 multiple myeloma cells treated with indicated concentrations of Spautin-1 or POM (1 M) for 24 hours. n = 3 biological replicates.

Spautin-1 reportedly suppresses autophagy by inhibiting the USP10 and USP13 deubiquitinases (14). IKZF1 protein levels were not decreased after small interfering RNAmediated down-regulation of USP10, alone or in combination with USP13 (fig. S7A), and Spautin-1s ability to down-regulate IKZF1 was not altered when one or both of these proteins were suppressed (fig. S7, B and C). Moreover, Spautin-1 down-regulated IKZF1 in 293FT cells in which autophagy was disabled by CRISPR-Cas9mediated disruption of ATG7, Beclin1, or FIP200 (fig. S8).

In contrast, down-regulation of IKZF1 by Spautin-1 was blocked by compounds that inhibit either the E1 ubiquitin activating enzyme or the proteasome (Fig. 4G). Down-regulation of IKZF1 by Spautin-1 was not, however, blocked by an inhibitor of neddylation, which is required for cullin-dependent ubiquitin ligases [e.g., the cereblon-containing ubiquitin E3 ligase that is coopted by the IMiDs (1, 2, 5)] (Fig. 4G). Down-regulation of exogenous IKZF1 by Spautin-1 requires the IKZF1 N-terminal region containing IKZF1s first zinc finger domain (ZF1) but not the IKZF1 zinc finger domain (ZF2) targeted by the IMiDs (fig. S9, A and B) (15, 16). The down-regulation of the N terminus of IKZF1 was similarly blocked by compounds that inhibit either the E1 ubiquitin activating enzyme or the proteasome but not by inhibitors of neddylation (fig. S9C). Preliminary structure-activity relationship studies identified both active and inactive Spautin-1 derivatives (fig. S10), suggesting that down-regulation of IKZF1 by Spautin-1 reflects a specific protein-binding event and that Spautin-1s potency and specificity can be optimized further.

The experiments described above implied that Spautin-1 posttranscriptionally regulates IKZF1. Nonetheless, Spautin-1 also suppressed exogenous IKZF1 mRNA levels in 293FT cells (fig. S11). However, Spautin-1 suppressed endogenous IKZF1 protein levels in KMS11 and L363 myeloma cells at concentrations that minimally suppressed IKZF1 mRNA levels (Fig. 4, H and I, and fig. S12, A to C). Spautin-1 did not down-regulate IKZF1 in all myeloma cells tested (fig. S12, D and E). The biochemical basis for this variability is not clear.

Notably, down-regulation of IKZF1 by Spautin-1 occurs much more slowly than with IMiDs, suggesting that its effect on IKZF1 is indirect (fig. S13). Nonetheless, its ability to score in a positive selection assay, as well as its inability to down-regulate IKZF1 in some myeloma lines, suggests that it is not broadly toxic at concentrations that down-regulate IKZF1. We are currently seeking the direct Spautin-1 target linked to IKZF1 turnover using genetic and biochemical tools.

One advantage of positive selection assays is their enablement of pooled screens. Our positive selection assay, however, uses a suicide gene. Some suicide genes cause bystander killing that could confound their use in pooled screens. In pilot studies, however, we confirmed that DCK*-IKZF1 cells rapidly outgrew DCK* cells in cocultures treated with IMiDs and BVdU (fig. S14A) and that the DCK* single guide RNA (sgRNA) was rapidly and specifically enriched relative to the control sgRNA in Cas9-positive 293FT cells expressing either DCK*-IKZF1 or DCK*-FOXP3 and then treated with BVdU (fig. S14, B and C). Therefore, bystander killing is negligible in this system.

To begin to address the general utility of our methodology, as well as its ability to function in a pooled format, we next did experiments with ASCL1 in place of IKZF1. ASCL1 is an undruggable lineage-specific transcription factor that is required for survival in many small cell lung cancers (SCLCs) and neuroblastomas (1719). We made Jurkat T cells that express Cas9 and either (i) DCK*, (ii) the neural/neuroendocrine lineagespecific transcription factor ASCL1, (iii) DCK*-ASCL1, or (iv) ASCL1-DCK* (Fig. 5A and fig. S15A). Jurkat cells were chosen because they are easily grown and expanded in suspension cultures. ASCL1-DCK* was chosen for further study because we could not generate cells producing high levels of DCK*-ASCL1 (fig. S15A). We first confirmed that ASCL1-DCK* expression sensitized Jurkat cells to BVdU and that this was partially reversed after down-regulating the fusion with ASCL1 sgRNAs (Fig. 5, A and B, and fig. S16, A and B). Cas9 expression was also slightly attenuated in the ASCL1-DCK* cells over time for unclear reasons (Fig. 5A). Nonetheless, these cells efficiently edited a GFP-based reporter of Cas9 activity within 10 days of receiving a GFP sgRNA (fig. S15, B and C). Next, we infected the ASCL1-DCK* and DCK* cells with a lentiviral sgRNA library targeting 788 genes (seven sgRNAs per gene) that encode druggable proteins (table S6). Ten days later (to allow time for gene editing), the cells were split and grown in the presence of 200 or 500 M BVdU for an additional 2 weeks (fig. S16C). We then determined sgRNA abundance by next-generation sequencing of genomic DNA and analyzed relative enrichment of sgRNAs compared to the time point before BVdU treatment (fig. S16D). We identified multiple sgRNAs against CDK2 that were markedly enriched at both BVdU concentrations in the ASCL1-DCK* cells but not the DCK* cells (Fig. 5C, fig. S16, D and E, and table S7).

(A) Immunoblot analysis of Jurkat cells first infected to express Cas9 and then superinfected to express exogenous ASCL1, DCK*, or the ASCL1-DCK* fusion. NCI-H69 cells are included as a benchmark for ASCL1 endogenous expression. (B) Growth inhibition (%), based on viable cell numbers relative to untreated controls, of the indicated cell lines from (A) treated with BVdU for 6 days. n = 2 biological replicates. (C) Hypergeometric analysis of BVdU positive selection CRISPR-Cas9 screen on day 25 relative to day 10 (early time point before BVdU treatment) of ASCL1-DCK* Cas9 Jurkat cells treated with 500 M BVdU. n = 2 biological replicates. (D) Quantification of fold change in mCherry:BFP ratio after 18 days of 500 M BVdU or DMSO (0) treatment of ASCL1-DCK* Cas9 Jurkat cells expressing the indicated sgRNAs and mCherry or a nontargeting control sgRNA and blue fluorescent protein (BFP) (initially mixed 1:3). n = 3 biological replicates. (E) Immunoblot and (F) RT-qPCR analysis of ASCL1-DCK* Cas9 Jurkat cells superinfected to express the indicated sgRNAs. n = 4 biological replicates. (G) Immunoblot analysis of Jurkat cells first infected with a lentivirus to stably express exogenous ASCL1, then infected with Dox-inducible (DOX-On) sgRNA-resistant CDK2 wild-type (WT) or CDK2 kinase-dead (KD) mutant, and lastly superinfected with a CDK2 or nontargeting sgRNA. Following superinfection with the sgRNA lentiviruses, cells were grown in DOX to maintain exogenous CDK2 expression. n = 4 biological replicates. Exo, exogenous CDK2; Endo, endogenous CDK2. Error bars represent SD. ns, nonsignificant; *P < 0.05; ***P < 0.001; ****P < 0.0001.

In validation studies, ASCL1-DCK* Jurkat cells expressing CDK2 sgRNAs outcompeted ASCL1-DCK* cells expressing control sgRNAs in the presence of BVdU but not in the presence of DMSO (Fig. 5D and fig. S16F). CDK2 sgRNAs also posttranscriptionally down-regulated ASCL1-DCK* protein levels in the Jurkat cells (Fig. 5, E and F) and endogenous, unfused, ASCL1 in human SCLC lines (NCI-H1876 and NCI-H2081) (Fig. 6, A and B, and fig. S18, A and B). Down-regulation of exogenous ASCL1 in Jurkat cells treated with a CDK2 sgRNA was rescued by an sgRNA-resistant CDK2 complementary DNA (cDNA) encoding wild-type CDK2 but not kinase-dead CDK2 (Fig. 5G). The kinase-dead CDK2 was, however, produced at slightly lower levels, presumably because it is less stable or because of its known dominant-negative effects due to cyclin sequestration (2022).

(A) Immunoblot and (B) RT-qPCR analysis of the NCI-H1876 SCLC cell line that endogenously expresses ASCL1 infected to express the indicated sgRNAs. n = 3 biological replicates. (C and E) Immunoblot and (D and F) RT-qPCR analysis of NCI-H1876 human SCLC cells (C and D) and 97-2 mouse SCLC cells (E and F) after treatment with the CDK2 PROTAC degraders (TMX-2138 and TMX-2172) or the indicated negative controls, all used at 500 nM for either 36 hours (C and D) or 8 hours (E and F). Neg Deg, negative control degrader ZXH-7035. n = 3 biological replicates. (G) Immunoblot analysis and (H) quantification of ASCL1 protein levels in 97-2 cells first treated with the CDK2 PROTAC degrader or negative control (500 nM) for 4 hours and then treated with cycloheximide (CHX) (150 g/ml) for the indicated times. S.E., short exposure; L.E., long exposure. n = 4 biological replicates. In all experiments, error bars represent SD except in (H), where error bars represent SEM. *P < 0.05; ***P < 0.001; ****P < 0.0001.

CDK2 has been well recognized as a potential anticancer target. The development of selective small-molecule CDK2 inhibitors, however, has been hampered by their off-target effects on other CDK family members, especially the broadly essential kinase CDK1. We verified that well-established CDK2 inhibitor dinaciclib (23) down-regulated both ASCL1 protein and mRNA levels (fig. S17, A to D), potentially due to its polypharmacological activity on both CDK2 and other CDKs such as CDK9 (23, 24). We obtained, however, two small-molecule CDK2 degraders (TMX-2138 and TMX-2172) that more selectively target CDK2 through recruitment of cereblon (25). Both of these compounds down-regulated ASCL1 protein levels in both human (NCI-H1876 and NCI-H1092) and mouse (97-2 and 188) SCLC lines (Fig. 6, C to F, and fig. S18, C to F). For unclear reasons, ASCL1 was down-regulated more rapidly in the mouse lines than in the human lines. We focused on TMX-2172 because TMX-2138 also suppressed ASCL1 mRNA levels in the mouse cells (Fig. 6F and fig. S18F). TMX-2172 decreased the half-life of ASCL1 protein (Fig. 6, G and H), consistent with posttranscriptional regulation of ASCL1 by CDK2.

We conducted our screens in IKZF1-independent 293FT cells rather than IKZF1-dependent myeloma cells and in ASCL1-independent Jurkat cells rather than in ASCL1-dependent SCLC cells in an attempt to preserve positive selection. It is possible, however, that some degradation mechanisms will be highly context dependent and restricted to the therapeutic target cell of interest. We also anticipate that some DCK* fusion proteins will not be functional due to steric or conformational effects. This might be remedied by fusing DCK* to the alternative POI terminus (N-terminus versus C-terminus), by exploring different linkers, or using alternative suicide proteins.

IMiDs are important multiple myeloma drugs, but loss of cereblon has emerged as an important mechanism of IMiD resistance (2628). Identification of Spautin-1s mechanism of action could eventually lead to drugs for circumventing this problem.

ASCL1 is a sequence-specific DNA binding transcription factor that would classically be deemed undruggable and serves as a lineage addiction oncoprotein in neural crestderived tumors, such as SCLCs and neuroblastomas (1719, 29). Genetic studies in Xenopus indicate that CDK2 regulates ASCL1 function and that ASCL1 contains multiple potential CDK2 phosphorylation sites that prevent it from inducing neuronal differentiation (30, 31). CDK2 is a potential dependency in some neuroblastomas (3234). CDK2 and N-MYC drive the accumulation of phosphorylated ASCL1 in undifferentiated neuroblastomas (31). Conversely, loss of CDK2 activity, such as through retinoic acidmediated induction of p27 or small-molecule inhibitors, is associated with neuroblastoma differentiation and decreased tumor formation (3237). It will be important to determine how, mechanistically, CDK2 regulates ASCL1 turnover. In particular, we have not yet shown that the regulation of ASCL1 by CDK2 is direct. Nonetheless, our study provides further support for CDK2 as a potential therapeutic target in SCLC and neuroblastoma.

The discovery that the IMiDs reprogram the cereblon ubiquitin E3 ligase for therapeutic benefit has galvanized interest in identifying compounds that can degrade, directly and indirectly, otherwise undruggable proteins. Sometimes, one can engineer heterobifunctional degrader molecules consisting of a POI-binding moiety, a linker, and a ubiquitin-ligase recruitment moiety (38). This approach requires a ligand with suitable binding affinity for the POI, and identifying a successful linker often requires multiple iterations of trial and error. Moreover, this approach fails to harness the many other ways a chemical could directly or indirectly degrade a protein, such as by inhibiting a deubiquitinating enzyme, displacing an interacting protein, or altering protein folding or subcellular localization. A trivial way to down-regulate proteins, especially those with naturally rapid turnovers, is to poison transcription or translation. The screening methodology described here should facilitate the characterization of designer degraders as well as enable mechanism-agnostic searches for compounds and targets that regulate the abundance of previously undruggable proteins.

293FT cells were originally obtained from the American Type Culture Collection (ATCC). 293AD cells were from Cell Biolabs. 293FT CRBN / cells were made by CRISPR-Cas9 editing (see below). 293FT and 293AD cells were maintained in Dulbeccos minimum essential medium (DMEM) supplemented with 10% fetal bovine serum (FBS), penicillin (100 U/ml), and streptomycin (100 g/ml). KMS11, KMS34, MM.1S, and L363 human multiple myeloma cells [gift of K. Anderson (Dana-Farber Cancer Institute)] and Jurkat cells (obtained from ATCC in September 2016) were maintained in RPMI medium supplemented with 10% FBS, penicillin (100 U/ml), and streptomycin (100 g/ml). NCI-H1876 (obtained in November 2016), NCI-H1092 (obtained in November 2018), and NCI-H2081 (obtained in November 2018) were obtained from ATCC. NCI-H1876, NCI-H1092, and NCI-H2081 cells were maintained in DMEM/F12 media supplemented with HITES [10 nM hydrocortisone (Sigma-Aldrich, #H0135), insulin (0.01 mg/ml), human transferrin (0.0055 mg/ml), sodium selenite (0.005 g/ml) (ITS, Gemini, #400-145), and 10 nM -estradiol (Sigma-Aldrich, #E2257)] and 5% FBS. The cell lines 188 and 97-2 were isolated from genetically engineered SCLC mouse tumors (see below for description of cell line generation) and maintained in RPMI 1640 media supplemented with HITES and 10% FBS. All cells were grown at 37C in the presence of 5% CO2. Fresh aliquots of cells were thawed every 4 to 6 months.

The following compounds were purchased: POM (Selleck, #S1567), LEN (Selleck, #S1029), MG132 (N-carbobenzyloxy-l-leucyl-l-leucyl-l-leucinal; Thermo Fisher Scientific, #47479020MG), MLN4924 (Active Biochem, #A-1139), MLN7243 (Thermo Fisher Scientific, #NC1129906), Spautin-1 (BioTechne; #5197/10), cycloheximide (VWR, #97064-724), BVdU (Chem-Impex International Inc., catalog no. 27735), actinomycin D (Thermo Fisher Scientific, #11805017), and dinaciclib (Selleck, #S2768).

CDK2 degraders. Synthesis and characterization of the small-molecule CDK2 degraders TMX-2138 and TMX-2172 and the negative degrader ZXH-7035 (structurally similar to the CDK2 binding region of TMX-2138 and TMX-2172 but lacking the cereblon recruiting element) are described previously (25).

293FT cells stably transduced with bicistronic lentiviruses expressing (i) a fusion between DCK* and the POI and (ii) GFP were seeded at a density of 0.25 106 cells/ml in 25 ml of media in a 15-cm dish (Corning, 353025). Two days later, the cells were counted and resuspended in media to a concentration of 10 106 cells/ml. The sample was passed through a mesh strainer (Thermo Fisher Scientific, #352235). The GFP fluorescence of the cells was analyzed by FACS using a Fortessa Aria II instrument. The brightest 1% of cells were collected in an Eppendorff tube, replated in a six-well dish, and expanded. This process was repeated three to four more times to isolate cells expressing the desired GFP levels.

Jurkat cells were first transduced with PLL3.7-Cas9-IRES-Neo. Neomycin-resistant cells with confirmed Cas9 expression were then superinfected with pLX304-ASCL1-DCK*-IRES-GFP or pLX304-DCK*-IRES-GFP, and transduced cells were selected with blasticidin. The blasticidin-resistant cells were then prepared for FACS sorting as above. In total, the brightest 1% of cells were FACS-sorted three times to isolate cells expressing the desired GFP levels. Jurkat cells expressing Cas9 and DCK*-FOXP3 were made in an analogous manner.

Cell pellets were lysed in a modified EBC lysis buffer [50 mM tris-Cl (pH 8.0), 250 mM NaCl, 0.5% NP-40, and 5 mM EDTA] supplemented with a protease inhibitor cocktail (cOmplete, Roche Applied Science, #11836153001). Whole-cell extracts were quantified using the Bradford protein assay. For experiments with 293FT cells, 10 g of protein per sample was boiled after adding 3 sample buffer (6.7% SDS, 33% glycerol, 300 mM dithiothreitol, and bromophenol blue) to a final concentration of 1; resolved by SDSpolyacrylamide gel electrophoresis (PAGE) using either 12.5% SDS-PAGE, Mini-Protean TGX 4 to 15% gels (Bio-Rad, #456-1086), or Criterion TGX gels (Bio-Rad, #5671085); semi-dry transferred onto nitrocellulose membranes; blocked in 5% milk in tris-buffered saline with 0.1% Tween 20 (TBS-T) for 1 hour; and probed with the indicated primary antibodies overnight at 4C. Membranes were then washed three times in TBS-T, probed with the indicated horseradish peroxidaseconjugated secondary antibodies for 1 hour at room temperature, and washed three times in TBS-T. Bound antibodies were detected with enhanced chemiluminescence Western blotting detection reagents [Immobilon (Thermo Fisher Scientific, #WBKLS0500) or SuperSignal West Pico (Thermo Fisher Scientific, #PI34078)]. The primary antibodies and dilutions used were as follows: rabbit anti-IKZF1 (Cell Signaling Technology, #5443S) at 1:1000, rabbit anti-V5 (Bethyl Laboratories, #A190-120A) at 1:1000, rabbit anti-DCK (Abcam, #151966) at 1:2000, rabbit anti-ASCL1 (Abcam, #ab211327) at 1:1000, rabbit anti-CDK2 (Cell Signaling Technology, #2546S) at 1:1000, mouse anti-P62 (Abcam, #ab56416) at 1:1000, rabbit antiLC3-I and LC3-II (Cell Signaling Technology, #3868S) at 1:1000, rabbit anti-ATG7L (Cell Signaling Technology, #8558S) at 1:1000, rabbit anti-Beclin1 (Cell Signaling Technology, #3495S) at 1:1000, rabbit anti-FIP200 (Cell Signaling Technology, #12436S) at 1:1000, rabbit -phospho-RB1 S795 (Cell Signaling Technology, #9301P) at 1:1000, mouse -RB1 4H1 (Cell Signaling Technology, #9309S) at 1:1000, mouse anti-actin (Sigma-Aldrich; clone AC-15, #A3854) at 1:25,000, mouse anti-Cas9 (Cell Signaling Technology, #14697) at 1:1000, mouse anti-vinculin (Sigma-Aldrich; #V9131) at 1:10,000, and mouse anti-actin (Cell Signaling Technology, #3700S) at 1:10,000. The secondary antibodies and dilutions used were goat anti-mouse (Pierce) at 1:10,000 and goat anti-rabbit (Pierce) at 1:5000.

A total of 750,000 293FT IKZF1-V5 cells per well were seeded in six-well dishes in a volume of 2 ml. On the next day, drugs to be added were diluted from a 10 mM stock (stored at 20C) into 0.5 ml of media before being added to the cells. The final volume in each well was then made up to 3 ml by adding a second drug in 0.5 ml or adding 0.5 ml of drug-free media.

Myeloma cells were seeded in 10-cm plates at a density of 0.75 106 cells/ml in a total volume of 8 ml. On the next day, the desired drug was diluted from a 10 mM stock (stored at 20C) into 1 ml of media, which was added to the intended well to achieve the desired final concentration. The final volume in each well was then made up to 10 ml by adding a second drug in 1 ml or adding 1 ml of drug-free media. After 24 hours, the cells were harvested for analysis.

293FT cells stably transduced with bicistronic lentiviruses encoding (i) IKZF1, DCK*, or DCK*-IKZF1 (IKZF1 cells, DCK* cells, and DCK*-IKZF1 cells, respectively) and (ii) GFP, as well as corresponding EV control cells, were seeded into six-well plates at 20,000 cells per well in 2.5 ml of media. The next day, 1 M BVdU dissolved in DMSO was diluted into media to prepare 6 stock solutions of BVdU at concentrations of 6 mM, 600 M, 60 M, 6 M, and 600 nM. For each stock solution, DMSO concentration was adjusted to a final concentration of 0.6%. Each well in the six-well dish received 0.5 ml of a 6 stock solution of BVdU to achieve final concentrations of 1 mM, 100 M, 10 M, 1 M, and 100 nM, respectively. A total of 0.5 ml of media with 0.6% DMSO was added to the sixth well as a control. Four days later, the cells were collected and counted using a Vi-Cell XR cell counter.

293FT cells were seeded as above at a density of 20,000 cells per well in 2 ml of media. A stock solution of 10 mM POM in DMSO was diluted into media to prepare a 6 M stock solution of POM. Cells received 0.5 ml of the 6 M POM stock solution to achieve an eventual final concentration of 1 M or 0.5 ml of control media. The next day, BVdU was added as described above, and cell proliferation was analyzed as above.

Jurkat cells expressing Cas9 and either ASCL1, ASCL1-DCK*, or DCK* alone were plated at 0.05 106 cells/ml per well in a 12-well plate and treated with increasing concentrations of BVdU (0, 1, 10, 100, 200, or 500 M). Six days later, the cells were counted using a Vi-Cell XR cell counter. For ASCL1 sgRNA rescue experiments, Jurkat cells expressing Cas9 and ASCL1-DCK* cells were superinfected with pLentiGuide-Purobased lentiviruses expressing sgRNAs targeting ASCL1 or a nontargeting sgRNA (sgCTRL). The cells were selected with puromycin, and expression of ASCL1 was analyzed by immunoblot analysis. The cells were then subjected to the BVdU assay as described above.

293FT cells stably transduced with bicistronic lentiviruses encoding (i) DCK*, DCK*-IKZF1, DCK*-K-RAS (G12V), DCK*-Cyclin D1, DCK*-PAX5, DCK*-FOXP3, and DCK*-MYC and (ii) GFP, as well as corresponding EV control cells, were seeded into 384-well plates (Corning, #3764) at 200 cells per well in 30 l of media. The next day, 1 M BVdU dissolved in DMSO was diluted into media to prepare 4 stock solutions of BVdU at concentrations of 4 mM, 2 mM, 1 mM, 400 M, 200 M, 100 M, 40 M, 20 M, 4 M, and 400 nM. On each plate, 10 l of each stock concentration of BVdU was added to two columns (32 wells) to achieve final concentrations of 1 mM, 500 M, 250 M, 100 M, 50 M, 25 M, 10 M, 5 M, 1 M, and 100 nM. Ten microliters of control media was added to four columns. Four days later, the cells were analyzed using an Acumen laser scanning cytometer (TTP Biosciences). GFP fluorescence was quantified by defining the metric GFP-positive object to identify GFP-positive cells while excluding debris or cell fragments.

Determination of Z. DCK* and DCK*-IKZF1 cells were seeded into 384-well plates (Corning, #3764) in 30 l of media at a density of 200 cells per well and allowed to adhere overnight. For each plate, an HPD300 dispenser (Hewlett-Packard) was used to add 4 nl of POM to a final concentration of 1 M to half the wells. An equal volume of DMSO was added to the other half of the plate. The next day, BVdU was added to the entire plate at a concentration of 10 M for the plate of DCK* cells and 100 M for the plate of DCK*-IKZF1 cells. Four days later, the cells were analyzed using an Acumen laser scanning cytometer (TTP Biosciences). The number of GFP-positive objects in each well was measured, and a Z statistic was calculated comparing the POM-treated wells to the DMSO-treated wells.

High-throughput chemical library screening. DCK* and DCK*-IKZF1 293FT cells were seeded into 384-well plates (Corning, #3764) at a density of 200 cells per well in a volume of 30 l of media. A custom-built Seiko Compound Transfer Robot was used to pin transfer 100 nl per well of small-molecule stock solutions from the wells of a drug library plate to the wells of assay plate, such that each well of the assay plate received a unique small molecule. An HPD300 non-contact dispenser (Hewlett-Packard) was used to dispense 100 nl of POM and dipyridamole into columns 23 and 24 and to add 100 nl of DMSO to columns 1 and 2. The final concentrations of POM and dipyridamole were 10 M and 12.5 M, respectively. The next day, 10 l of BVdU stock solution was added to columns 2 to 24 of each of the DCK-IKZF1 and DCK* assay plates, respectively. The concentration of the BVdU stock solution was calculated to achieve the desired final concentration of BVdU (10 M in DCK* assay plates and 100 M in DCK*-IKZF1 assay plates) in the well.

After 4 days, the GFP fluorescence of each assay plates was quantified using an Acumen scanning laser cytometer. For each plate, the average and SD of the GFP fluorescence of wells in columns 3 to 22 were calculated. For each well on an assay plate, the GFP fluorescence was converted to a z score using the formula: z(well) = [GFP (well) GFP (plate)] / GFP (plate), where GFP (plate) is the mean GFP fluorescence for that plate and GFP (plate) is the SD for that plate.

High-throughput chemical library screening (in-well competition assay). DCK*-IKZF1 (GFP) and DCK* (Td) cells were mixed together in a 1:1 ratio and then seeded into 384-well plates (Corning, #3764) at a density of 400 cells per well in 30 l of media. Pin transfer from IMiD derivative library plates and dispensation of POM and dipyridamole were performed as described above. The next day, 10 l of BVdU stock solution was added to columns 2 to 24 of each plate to achieve a final concentration of 100 M. After 4 days, the GFP and TdTomato fluorescence of each assay plate was quantified using an Acumen scanning laser cytometer. For each well, the ratio of GFP/tdTomato fluorescence was calculated and normalized to the values in the well that received DMSO and BVdU. The resulting values were converted to a heatmap using Morpheus (Broad Institute).

Determination of Z. 293FT IKZF1-Fluc cells were seeded into 96-well plates at a density of 2000 cells per well in a volume of 50 l of media and incubated overnight at 37C. The next day, an additional 50-l media and POM (final concentration of 2 M) was added to 30 wells of the plate (rows B to G, columns 2 to 6). Control media containing DMSO was added to 30 wells of the plate (rows B to G, columns 7 to 11). A Dual-Glo assay (Promega) was performed by first aspirating all media from the tissue culture plates. Twenty-five microliters of a 1:1 dilution of Dual-Glo luciferase assay reagent in phosphate-buffered saline (PBS) was added to wells and incubated for 10 min. Luminescent signal was measured with a plate reader. Stop & Glo reagent (12.5 l) was then added to the wells, incubated for 10 min, and luminescent signal was measured. The average Fluc/Rluc ratio for cells treated with DMSO and POM was calculated, and a Z statistic was calculated.

High-throughput library screening using Fluc/Rluc readout. IKZF1-Fluc assay plates were generated by plating 293FT IKZF1-Fluc cells into 384-well plates. For the 8-hour treatment arm, cells were plated at a density of 4000 cells per well. A custom-built Seiko Compound Transfer Robot was used to pin transfer 100 nl per well of small molecule from the drug library plate to the assay plate, such that each well of the assay plates received a unique small molecule. After 8 hours, the plates were shaken out and blotted on clean paper towels to remove the media. A Thermo Multidrop Combi was used to dispense 20 l of a 1:1 dilution of Dual-Glo luciferase reagent, and the plates were shaken for 10 min. Firefly luciferase signal was quantified using an EnVision plate reader. A Thermo Multidrop Combi was used to dispense 10 l of Dual-Glo Stop + Glo reagent, and the plates were shaken for 10 min. Renilla luciferase signal was quantified using an EnVision plate reader. For each plate, the ratios of the Firefly/Renilla luciferase signals were converted to a Z-distribution as outlined above. For the 4-day treatment arm, the experiment was performed in an analogous manner, but the cells were plated at a density of 200 cells per well and were incubated for 4 days before analysis.

Gene-targeting sgRNAs and appropriate controls were designed using the rule set described at the Genetic Perturbation Program (GPP) portal (http://portals.broadinstitute.org/gpp/public). Oligonucleotides were flanked by polymerase chain reaction (PCR) primer sites, and PCR was used to amplify DNA using NEBNext kits. The PCR products were purified using Qiagen PCR cleanup kits and cloned into pXPR_BRD003 using Golden Gate cloning reactions. Pooled libraries were amplified using electrocompetent Stbl4 cells. Viruses were generated as outlined at the GPP portal. The sgRNA library (CP1080, M-AB34) was custom-designed to target cancer-relevant druggable genes. It consisted of 5566 sgRNAs targeting 788 genes (7 sgRNAs targeting each gene) and 300 nontargeting sgRNAs as controls (table S6).

Jurkat cells that had been infected with PLL3.7-Cas9-IRES-Neo and subsequently maintained in G418 were then superinfected with pLX304 ASCL1-DCK*-V5-IRES-GFP or pLX304 DCK*-V5-IRES-GFP and placed under blasticidin selection. Blasticidin-resistant cells were sorted for GFP expression (top 1%) three times by FACS. Protein abundance of ASCL1-DCK* or DCK* alone was confirmed by immunoblot analysis, and functionality of ASCL1-DCK* or DCK* alone was determined using BVdU sensitivity and rescue experiments with sgRNAs targeting ASCL1. Cas9 expression was confirmed by immunoblot analysis, and Cas9 activity was confirmed using a Cas9 GFP reporter [pXPR_011 (Addgene, #59702)] (39) that showed near maximal editing 10 days after infection.

On day 0, ASCL1-DCK* and DCK* cells expressing Cas9 were expanded and then counted. For each line, 2.2 107 cells (4000 cells per sgRNA) were pelleted and resuspended at 2 106 cells/ml in media supplemented with polybrene (8 g/ml) and infected at a multiplicity of infection (MOI) of ~0.3 with the sgRNA druggable library (CP1080, M-AB34) described above. The cells mixed with polybrene and virus were then plated in 1-ml aliquots onto 12-well plates and centrifuged at 434g for 2 hours at 30C. Sixteen hours later (day 1), the cells were collected, pooled, and centrifuged to remove the virus and polybrene, and the cell pellet was resuspended in complete media at 2 105 cells/ml and plated into nontissue culturetreated t175 flasks. The cells were then cultured for 48 hours before being placed under puromycin (1 g/ml) drug selection at 4 105 cells/ml.

A parallel experiment was performed on day 3 to determine the MOI. To do this, the cells infected with the sgRNA library and mock-infected cells were plated at 4 105 cells/ml in the presence or absence of puromycin. After 72 hours (day 6), cells were counted using the Vi-Cell XR cell counter, and the MOI was calculated (which ranged from 0.2 to 0.3 for each replicate) using the following equation: (# of puromycin-resistant cells infected with the sgRNA library / # total cells surviving without puromycin after infection with the sgRNA library) (# of puromycin-resistant mock-infected cells / # total mock-infected cells).

On day 6 after MOI determination, puromycin-resistant cells were pooled, collected, and counted, and 1 108 cells were replated at a concentration of 4 105 cells/ml in complete media containing puromycin (1 g/ml). The remaining cells were discarded. On day 8, again, the puromycin-resistant cells were pooled, collected, and counted, and 1 108 cells were replated at a concentration of 4 105 cells/ml in complete media containing puromycin (1 g/ml).

On day 10, puromycin-resistant cells were pooled, collected, and counted. A total of 2 107 cells were collected and washed in PBS, and the cell pellets were frozen for genomic DNA isolation for the initial time point before BVdU selection. Then, 2 107 cells were resuspended in complete media (now without puromycin) containing either 200 or 500 M BVdU at a final concentration of 5 104 cells/ml and plated into t175-cm flasks. Thus, at least 1000 cells per sgRNA were introduced into BVdU selection.

On day 15, cells treated with 200 or 500 M BVdU were collected and counted. A total of 10 106 cells from each arm of the screen were then resuspended in complete media containing either 200 or 500 M BVdU at a final concentration of 5 104 cells/ml and plated into t175-cm flasks. The remaining cells were centrifuged and washed in PBS, and the cell pellets were frozen. Again, at least 1000 cells per sgRNA were maintained under BVdU selection.

On day 20, cells treated with 200 or 500 M BVdU were collected and counted. A total of 10 106 cells from each arm of the screen were then resuspended in complete media containing either 200 or 500 M BVdU at a final concentration of 5 104 cells/ml and plated into t175-cm flasks. If available, the remaining cells were centrifuged and washed in PBS, and the cell pellets were frozen. Again, at least 1000 cells per sgRNA were maintained under BVdU selection.

On day 25, all remaining cells were collected and counted. The remaining cells were divided in aliquots of 6 106 cells (which corresponds to 1000 cells per sgRNA) and washed in PBS, and the cell pellets were frozen for genomic DNA isolation for the final time point after BVdU selection. The screen was performed in two biological replicates.

Following completion of the screen, genomic DNA was isolated using a Qiagen Genomic DNA midi prep kit (catalog no. 51185) according to the manufacturers protocol. Raw Illumina reads were normalized between samples using log2[(sgRNA reads/total reads for sample) 1 106 + 1]. The initial time point data (day 10) were then subtracted from the end time point after BVdU selection (day 25) to determine the relative enrichment of each individual sgRNA after BVdU treatment using hypergeometric analysis and the STARS algorithm. A q value cutoff of <0.25 was used to call hits. The averaged data from two biological replicates were used for all analyses.

293FT cells stably transduced with bicistronic lentiviruses encoding (i) DCK*-IKZF1 and GFP and (ii) DCK* and TdTomato were mixed together at a ratio of 1:99. Pooled cells were plated at a density of 20,000 cells per well of a six-well plate and in a total volume of 2 ml of media. Cells received 0.5 ml of the 6 M POM stock solution to achieve an eventual final concentration of 1 M or 0.5 ml of control media. The next day, the cells received 0.5 ml of 600 M BVdU stock solution or 0.5 ml of control media. Cells were collected for FACS analysis on days 0, 3, 6, 10, and 14. After each time point, cells were reseeded at 20,000 cells per well and treated with fresh BVdU (or DMSO).

DCK*-IKZF1 293FT cells were infected with a mixture of two lentiviruses encoding Cas9 and either (i) sgDCK and mCherry or (ii) sgCTRL and BFP (blue fluorescent protein). The two lentiviruses were mixed together such that the ratio of mCherry-positive to BFP-positive cells after infection and puromycin selection was 1:99. An analogous experiment was set up using a lentivirus encoding sgCTRL and mCherry. The pool of infected cells was plated at 20,000 cells per well in a six-well plate and then cultured in media containing either 100 M BVdU or DMSO for 21 days. Cells were collected for FACS analysis on days 1, 6, 18, and 33. After each time point, cells were reseeded at 20,000 cells per well and treated with fresh BVdU.

Jurkat cells that had been stably infected to express Cas9 and DCK*-FOXP3 were superinfected with lentivirus encoding either (i) sgDCK and mCherry or (ii) sgCTRL and BFP. These cells were mixed together and analyzed by FACS to achieve a final ratio of mCherry-positive to BFP-positive cells of 1:99. The pool of infected cells was plated at 40,000 cells/ml in a six-well plate and then cultured in media containing either 100 M BVdU or DMSO for 14 days. Cells were collected for FACS analysis on days 6 and 14.

The Jurkat cells expressing Cas9 and ASCL1-DCK* that were used for the CRISPR-Cas9 screen described above were superinfected with lentiviruses encoding sgRNAs targeting CDK2, ASCL1, or a nontargeting sgRNA as a control, the fluorescent protein mCherry and a puromycin resistance gene or with a lentivirus encoding a nontargeting sgRNA as a control, and the fluorescent protein BFP and a puromycin resistance gene (see schema in fig. S16F). The cells were selected with puromycin. mCherry puromycin-resistant cells were then mixed with BFP puromycin-resistant cells at a 1:3 ratio as determined by FACS analysis. The mixed cells were plated at 5 104 cells/ml and then cultured in media containing 500 M BVdU or DMSO (0) for 18 days. FACS analysis was performed every 6 days. After each FACS analysis, fresh BVdU was added, and the density of the cells was adjusted to 5 104 cells/ml with fresh media.

Cells were counted using a Vi-Cell XR cell counter and were plated at a concentration of 4 105 cells/ml per well for NCI-H1092, 188, and 97-2 SCLC cell lines or at 1 106 cells/ml per well for the NCI-H1876 SCLC cell line in six-well plates. Cells were then treated with the CDK2 degraders TMX-2138, TMX-2172, or the negative degrader ZXH-7035 (Neg Deg) at 500 nM for 36 hours for NCI-H1092 and NCI-H1876 human SCLC cell lines or 8 hours for 188 and 97-2 mouse SCLC cell lines. For half-life time determination with cycloheximide, 97-2 cells were treated with CDK2 degraders for 4 hours before the addition of cycloheximide at 150 g/ml. Cells were harvested at the indicated times after addition of cycloheximide.

293FT cells were seeded in six-well plates at a density of 750,000 cells per well in 2.5 ml of media per well. The next day, the cells were treated with the indicated concentrations of Spautin-1, POM, or DMSO for 24 hours. Multiple myeloma cells were seeded in 10-cm plates at a density of 0.75 106 cells/ml in a total volume of 9 ml of media. The next day, the cells were treated with the indicated concentrations of Spautin-1, POM, or DMSO. RNA was extracted using an RNeasy mini kit (Qiagen, #74106) according to the manufacturers instructions. RNA concentration was determined using the NanoDrop 8000 (Thermo Fisher Scientific). cDNA was generated by reverse transcription using the AffinityScript qPCR (quantitative PCR) cDNA Synthesis kit (Agilent, 600559) according to the manufacturers instructions. qPCR was performed using the LightCycler 480 (Roche) with the LightCycler 480 Probes Master Kit (Roche) and TaqMan probes (Thermo Fisher Scientific) according to the manufacturers instructions. The Ct values for each probe were then normalized to the Ct value of ACTB for that sample. The data from each experiment were then normalized to the control to determine the relative fold change in mRNA expression. The following TaqMan probes were used: Hs00958474_m1 (IKZF1 human), ASCL1 human Hs04187546_g1 for detection of endogenous ASCL1, ASCL1 human Hs05000540_s1 for detection of the exogenous ASCL1-DCK* fusion, ACTB human Hs01060665_m1, Ascl1 mouse Mm03058063_m1, and Actb mouse Mm00607939_s1. All quantitative calculations were performed using the 2Ct method using Beta Actin (ACTB) as a reference gene.

For the positive selection small-molecule screen, GFP fluorescence for each well was normalized to untreated wells. For each library drug, normalized GFP fluorescence was plotted as a function of library drug concentration. Each drug treatment was performed in duplicate. Data were analyzed and plotted using GraphPad Prism v6, median inhibitory concentration (IC50) values were determined using the log (inhibitor) versus response -- Variable slope (four parameters) analysis module, and area under the curve (AUC) values were determined using the AUC analysis module (13). For the positive selection CRISPR-Cas9 BVdU resistance screen, the relative fold enrichment of each individual sgRNA after BVdU treatment was calculated using both Broad Institutes hypergeometric analysis and the STARS algorithm to determine a rank list of candidate ASCL1 stabilizer genes ranked by q value, where statistical significance is q < 0.25.

For all other experiments, statistical significance was calculated using unpaired, two-tailed Students t test. P values were considered statistically significant if the P value was <0.05. For all figures, *P < 0.05, **P < 0.01, ***P < 0.001, and ****P < 0.0001. Error bars represent SD unless otherwise indicated.

Acknowledgments: We thank the members of the Kaelin and Oser laboratories for helpful discussions. Special thanks to the ICCB (Longwood) at HMS for assistance with small-molecule screens, to W. Gao for generation of adenoviral vectors used for recombination cloning, and D. Hong for generation of SCLC mouse cell lines. Funding: W.G.K. is supported by an NIH R35 grant and is an HHMI investigator. M.G.O. is supported by a Damon Runyon Cancer Research Foundation Clinical Investigator Award and an NCI/NIH KO8 grant (no. K08CA222657). V.K. is supported by an American Society of Hematology Research Training Award and T32 NIH Training Grant CA009172. J.A.P. is funded by an NIGMS grant R01 GM132129. E.S.F. is funded by NCI R01CA2144608. N.S.G. is funded by NIH R01 CA214608-03. M.I. is supported by an Internationalisation Fellowship from the Carlsberg Foundation. C.J.O. is supported by an NIH/NCI Pathway to Independence Award (R00CA190861). Author contributions: V.K., L.D., and B.L.L. performed experiments and, together with W.G.K. and M.G.O., designed experiments, analyzed data, and assembled and wrote the manuscript. J.A.M. helped design experiments. A.C.W. and A.H.S. performed experiments. M.I. designed and synthesized the IMiD library. J.P. and C.J.O. measured CRBN binding and cellular activity of candidate IMiDs; J.B. supervised these experiments. I.S.H. and J.E.E. constructed the Ludwig anticancer and antimetabolite libraries and helped analyze data from the screen. E.D., X.L., and S.J.B. synthesized and characterized Spautin-1 derivatives. J.A.P. performed TMT global proteomic profiling of Spautin-1. S.P.G. supervised these experiments. K.A.D. and E.S.F. analyzed TMT proteomic data. K.J.B. determined the half-lives of luciferase fusion proteins and the Z of the dual-luciferase system. J.G.D. helped analyze data from the CRISPR screen. M.T., T.Z., and N.S.G. helped generate and validate CDK2 degraders. Competing interests: W.G.K. has financial interests in Lilly Pharmaceuticals, Fibrogen, Agios Pharmaceuticals, Cedilla Therapeutics, Nextech Invest, Tango Therapeutics, and Tracon Pharmaceuticals. N.S.G. is a founder, science advisory board member, and equity holder in Gatekeeper, Syros, Petra, C4, B2S, Aduro, and Soltego (board member). E.S.F. is a founder, scientific advisory board (SAB) member, and equity holder of Civetta Therapeutics, Jengu Therapeutics (board member), and Neomorph Inc. E.S.F. is an equity holder of C4 Therapeutics. E.S.F. consults or has consulted for Novartis, AbbVie, Astellas, Deerfield, EcoR1, and Pfizer. The Fischer laboratory receives or has received research funding from Novartis, Deerfield, and Astellas. The Gray laboratory receives or has received research funding from Novartis, Takeda, Astellas, Taiho, Janssen, Kinogen, Voronoi, Her2llc, Deerfield, and Sanofi. M.G.O. has sponsored research agreements with Lilly Pharmaceuticals and Takeda Pharmaceuticals. V.K. has consulted for Cedilla Therapeutics. S.J.B. is on the SAB of Adenoid Cystic Carcinoma Foundation. J.B. is an employee, executive, and shareholder of Novartis AG (Basel, Switzerland). J.G.D. consults for Agios, Foghorn Therapeutics, Maze Therapeutics, Merck, and Pfizer; J.G.D. consults for and has equity in Tango Therapeutics. J.G.D.s interests were reviewed and are managed by the Broad Institute in accordance with its conflict of interest policies. I.S.H. is a consultant for ONO Pharmaceuticals (USA). V.K. and W.G.K. are inventors on a patent application on positive selection assays to identify protein degraders, which was filed by the Dana-Farber Cancer Institute (U.S. patent application number 16/332,921, filed on 13 March 2019 and published on 1 August 2019). N.S.G., M.T., and T.Z. are named inventors on patent applications covering Cdlk2 degraders described in the paper, and which were filed by the Dana Farber Cancer Institute (U.S. Provisional Application No. 62/829,302, filed April 4, 2019 and U.S. Provisional Application No: 62/981,334, filed February 25, 2020). The authors declare that they have no other competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper may be requested from the authors. All plasmids are available from the authors.

See the article here:
Targeting oncoproteins with a positive selection assay for protein degraders - Science Advances

Recommendation and review posted by Alexandra Lee Anderson

Programming in the pandemic – Perforce: In open source, crowd is a positive – ComputerWeekly.com

The Computer Weekly Developer Network examines the impact of Covid-19 (Coronavirus) on the software application development community.

With only a proportion of developers classified as key workers (where their responsibilities perhaps included the operations-side of keeping mission-critical and life-critical systems up and online), the majority of programmers will have been forced to work remotely, often in solitude.

So how have the fallout effects of this played out?

This post comes from Justin Reock in his role as chief evangelist for open source software (OSS) & Application Programming Interface (API) management at Perforce Software.

Reock reflects upon the use of open source platforms, languages and related technologie in general in light of the Covid-19 global crisis and writes as follows

On the whole, I would argue that open source software has been invaluable during the pandemic.

Crowd-sourced software initiatives and hackathons, protein-folding peer-to-peer networks and foundation sponsorship have all been in play throughout the contagion and many of these initiatives continue forwards.

GitHub has shown us that commits held steady or even increased suggesting (if it is fair to measure that in terms of raw commits without considering quality) that developer productivity has held steady or even gone up.

For many developers, having a shared project and sense of community during a very isolating time for humanity has been uplifting and good for their spirits. Its a reminder that coding together is in fact a social activity, no different than any other collaborative and creative endeavour.

Perhaps the biggest impact and fallout from this whole period of experiences (for programmers, operations staff and the wider software engineering community) will be the acceleration of transformation and DevOps initiatives within businesses.

So many have witnessed the resilience of businesses that have already undergone the DevOps transition (and even watched their profits soar) as we moved to online ordering, contactless delivery and more.

The CI/CD part of the DevOps makeover has always been about dealing with constant change.The mantra of releases are hard, so release often embraces the notion that change is difficult, so organisations should make themselves really good at dealing with it. That meant when the pandemic hit, the seams of our global digital twin were tested. Companies that were capable of quickly refactoring to online experiences, digital goods and other conveniences have now become essential to carrying on a reasonable quality of life in the physical world.

It is one thing to expect the unexpected, and it is quite another to design systems that thrive in unexpected conditions.Whatever requisite effort may need to be invested to achieve DevOps maturity in an organisation, the positive impact it can have to business longevity is now indisputable.

However, especially in segments of the industry that are highly collaborative such as gaming, quality and deadlines have suffered drastically and development teams have blamed it squarely on moving to a remote work model.

NOTE: As a software change management specialist, Perforce has a particularly acute proximity with and close understanding of how games programmers work.

Even enabling employees to work from home was a challenge, as the hardware supply chain which we rely on to deliver our webcams, tablets, and laptops and other tech gear suffered major disruptions: so, all in all, there is no question that organisations, including open source communities, which had already taken steps towards transformation and remote work were able to continue operations smoothly, though not completely without impact.

That said, the overall industry picture is not all rosy, with many segments that rely heavily on peer collaboration taking a hit in quality and productivity.

We hope, of course, for brighter future times for all.

Reock: Commit to commit dear developers, you know you want to.

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Synthetic Biology Startup Acquires AI Platform To Disrupt The Drug Industry – Forbes

Sean McClain, Co-Founder and CEO of AbSci.

There has been a lot of recent attention on the challenges of delivering COVID-19 vaccines. But there are also challenges in making them. For some of the newer options like those from Johnson & Johnson and Oxford-AstraZeneca, the modified cells used in vaccine production are struggling under the scale of demand. But synthetic biology company AbScis recent acquisition of the artificial intelligence platform, Denovium, could help mitigate this type of challenge in the future.

Unlike mRNA vaccines, the Johnson & Johnson/Oxford-AstraZeneca class of vaccines rely on a type of virus called adenovirus which is known to cause colds in chimpanzees. To address COVID-19, the adenovirus is genetically altered to express the SARS-CoV-2 spike protein which is what ultimately triggers the bodys immune response. Like mRNA vaccines, adenovirus-based vaccines train the body to recognize and fight COVID-19, foregoing the need to inject a person with a weakened version of SARS-CoV-2.

But producing enough adenovirus cells has been a challenge. To make vaccine doses, large volumes of altered adenovirus are produced by replicating cells in bioreactors. But, the scale of production can also cause the cells to weaken. This can result in a reduced output of adenovirus copies. So while these new vaccines may represent a breakthrough in adenovirus-based therapeutics, the process also highlights some critical roadblocks.

One major issue is that drug discovery and drug manufacturing are often disconnected from one another. Drug discovery typically starts with screeningthe process of finding a set of compounds out of 100,000 combinations that can best neutralize a targeted weak point of a disease. But when a promising protein is identified, it often turns out to be difficult to scale effectively.

Once a therapeutic compound is identified, researchers must then determine if it works well with a group of similar cells called a cell-line. By inserting the compound into the cellswhich then divide and multiply in a bioreactorthe cells act like factories to produce greater volumes of the compound of choice. But, as in the case with adenovirus-producing cells, not all cells can maintain their functions at large volumes. If the protein compound doesnt work well in a scalable cell-line, researchers often have to go back to the drawing board to find a new compound and start again.

Many in the biopharma space are aware of this inefficient process. The synthetic biology company AbSci has spent years developing a platform solution that streamlines the workflow. [Our platform] is simultaneously a drug discovery and manufacturing platform that allows you to discover your drug and the cell line that can manufacture [it], says AbSci CEO, Sean McClain. Were finally uniting drug discovery and manufacturing the first time.

AbSci refers to their core process as their Protein Printing platform, not because it uses ink and paper to make proteins but as an analogy for ease and speed. The first technology [in our platform] is our SoluPro E. coli strain. It has been highly engineered to be more mammalian-like to be able to produce mammalian-like proteins that E. coli wasn't previously capable of doing, says McClain. AbSci also uses what the company calls a folding solution to precisely tailor how proteins fold and therefore function.

Imad Ajjawi, Co-Founder and CBO of Denovium

To find the most effective protein, AbSci alters its folding solutions to create as many protein varieties as possible, often to the order of 10s of millions. The more protein types available, which AbSci refers to as libraries, the higher the likelihood of success. But this also creates a challenge: so many options, but which to choose?

To address this, AbSci recently acquired artificial intelligence company, Denovium. By integrating Denoviums AI platform, AbSci can improve its data analysis via AI models. From there, the company can take the best candidates and find the most effective cell-line to produce the chosen compounds at scale. McClain explains that traditional drug discovery and manufacturing typically takes years. But AbScis platform can take that timeline down to weeks. Were actually able to manufacture [therapeutics] because the dirty secret in pharma is that so many drugs get shelved because [pharma companies] can't actually manufacture them, says McClain.

For McClain, acquiring Denovium is a big step forward for AbScis discovery process. Its going to change the paradigm. Its really a perfect marriage of both data and AI technology. If you don't have good data feeding into your AI model, it's worthless. But if you don't have an AI technology, you can't mine [the data] and get all the benefits, says McClain.

Denoviums co-founder and CBO, Imad Ajjawi, also sees the new collaboration as a significant opportunity. It's really exciting to be a part of AbSci because they have all the data, billions of points that the deep learning engine can now analyze, says Ajjawi. AbScis acquisition also comes on the heels of the companys $65 million Series E in late 2020.

Upgrading the union of biology and AI is important for advancing synthetic biology innovation. But the true potential beneficiaries of this advanced discovery platform are those in need of novel drug options.

AbScis main goal as a company is to bring therapeutics to market more quickly. This technology's impact on healthcare is profound because more drugs and biologics can now enter patients' hands faster, says McClain.

McClain believes that AbScis technology will help speed the process of clinically testing new medications. Faster clinical trial turnarounds could increase the number of drugs approved to address a range of diseases. This could be most impactful for patients with rare or difficult to treat conditions as drug discovery is often prioritized based on how long it takes to find a scalable cell-line.

But though AbSci is working to accelerate drug discovery, the process still takes time. Right now, we have six drugs that are in preclinical or clinical trials. And one of them is actually in phase three. So we could have an improved product here in the next couple of years, says McClain.

As Absci and Denovium finalize their technology integrations, McClain is also looking ahead to build as many partnerships as possible. The more partnerships we do, the more patients were able to affect that at the end of the day, says McClain.

In line with that goal, AbSci today announced a continuation of its partnership with Astellas and Xyphos. AbSci will take on screening and identifying an optimal cell-line for a leading variant of Xyphos MicAbody, a bispecific antibody-like adaptor molecule used in the company's immuno-oncology program.

McClain expects more partnership announcements will follow in the first quarter of 2021. We have some really exciting partnerships that are going to be coming out over this next quarter that I think speak to the [range] of the types of disease states we're working on and the breadth of how the technology can be used within biopharma, says McClain.

Im the founder of SynBioBeta, and some of the companies that I write about are sponsors of the SynBioBeta conference and weekly digest, including AbSci. Thank you to Fiona Mischel and Vinit Parekh for additional research and reporting in this article.

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Synthetic Biology Startup Acquires AI Platform To Disrupt The Drug Industry - Forbes

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TYME Granted U.S. Patent Claims Covering Use of TYME-19 to Treat COVID-19 Infections – Business Wire

BEDMINSTER, N.J.--(BUSINESS WIRE)--Tyme Technologies, Inc. (NASDAQ: TYME), an emerging biotechnology company developing cancer metabolism-based therapies (CMBTs), announced that it has received notification that the United States Patent and Trademark Office has granted additional patent claims related to the Companys metabolomic technology platform. The patent, U.S. Patent No. 10,905,698, is directed to methods for treating COVID-19.

Unlike immune therapies that depend upon the structure of the external virus coat of COVID-19 where the therapy directs its attack, we believe TYME-19 is agnostic to this structure and any mutations to the viral coat. Like other TYME agents, TYME-19 affects cellular metabolism. It constrains viral replication after a virus has inserted its genetic blueprint into an infected cell by inhibiting the ability of the virus to use the cells synthetic apparatus to make viral proteins and lipids. As a result, we believe that TYME-19 diminishes the ability of COVID-19 to hijack an infected cell. TYME intends to initiate the appropriate clinical trials to substantiate the safety and efficacy of TYME-19.

TYME-19 is an investigational compound that is not approved in the U.S. for any disease indication.

About TYME-19

TYME-19 is an oral synthetic member of the bile acid family that the Company also uses in its anticancer compound, TYME-18. Because of its expertise in metabolic therapies, the Company was able to identify TYME-19 as a potent, well characterized antiviral bile acid and has performed preclinical experiments establishing effectiveness against COVID-19. Bile acids have primarily been used for liver disease; however, like all steroids, they are messenger molecules that modulate a number of diverse critical cellular regulators. Bile acids modulate lipid and glucose metabolism and can remediate dysregulated protein folding, with potentially therapeutic effects on cardiovascular, neurologic, immune, and other metabolic systems. Some agents in this class also have antiviral properties. In preclinical testing, TYME-19 repeatedly prevented COVID-19 viral replication without attributable cytotoxicity to the treated cells. Previous preclinical research has also shown select bile acids like TYME-19 have had broad antiviral activity.

About Tyme Technologies

Tyme Technologies, Inc., is an emerging biotechnology company developing cancer therapeutics that are intended to be broadly effective across tumor types and have low toxicity profiles. Unlike targeted therapies that attempt to regulate specific mutations within cancer, the Companys therapeutic approach is designed to take advantage of a cancer cells innate metabolic weaknesses to compromise its defenses, leading to cell death through oxidative stress and exposure to the bodys natural immune system.

With the development of TYME-18 and TYME-19, the Company believes that it is also emerging as a leader in the development of bile acids as potential therapies for cancer and COVID-19. For more information, visit http://www.tymeinc.com. Follow us on social media: Facebook, LinkedIn, Twitter, YouTube and Instagram.

Forward-Looking Statements/Disclosure Notice

In addition to historical information, this press release contains forward-looking statements under the Private Securities Litigation Reform Act that involve substantial risks and uncertainties. Such forward-looking statements within this press release include, without limitation, statements regarding our drug candidates (including SM-88 and TYME- 18) and their clinical potential and non-toxic safety profiles, our drug development plans and strategies, ongoing and planned preclinical or clinical trials, including the proposed TYME-19 proof-of-concept study, preliminary data results and the therapeutic design and mechanisms of our drug candidates. The words believes, expects, hopes, may, will, plan, intends, estimates, could, should, would, continue, seeks, anticipates, and similar expressions (including their use in the negative) are intended to identify forward-looking statements. Forward-looking statements can also be identified by discussions of future matters such as: the effect of the novel coronavirus (COVID-19) pandemic and the associated economic downturn and impacts on the Company's ongoing clinical trials and ability to analyze data from those trials; the cost of development and potential commercialization of our lead drug candidate and of other new products; expected releases of interim or final data from our clinical trials; possible collaborations; and the timing, scope, status, objectives and strategy of our ongoing and planned trials; the success of management transitions; and other statements that are not historical. The forward-looking statements contained in this press release are based on managements current expectations and projections which are subject to uncertainty, risks and changes in circumstances that are difficult to predict and many of which are outside of our control. These statements involve known and unknown risks, uncertainties and other factors which may cause the Companys actual results, performance or achievements to be materially different from any historical results and future results, performance or achievements expressed or implied by the forward-looking statements. These risks and uncertainties include but are not limited to: the severity, duration, and economic and operational impact of the COVID-19 pandemic; that the information is of a preliminary nature and may be subject to change; uncertainties inherent in the cost and outcomes of research and development, including the cost and availability of acceptable-quality clinical supply, and in the ability to achieve adequate start and completion dates, as well as uncertainties in clinical trial design and patient enrollment, dropout or discontinuation rates; the possibility of unfavorable study results, including unfavorable new clinical data and additional analyses of existing data; risks associated with early, initial data, including the risk that the final data from any clinical trials may differ from prior or preliminary study data; final results of additional clinical trials that may be different from the preliminary data analysis and may not support further clinical development; that past reported data are not necessarily predictive of future patient or clinical data outcomes; whether and when any applications or other submissions for SM-88 may be filed with regulatory authorities; whether and when regulatory authorities may approve any applications or submissions; decisions by regulatory authorities regarding labeling and other matters that could affect commercial availability of SM-88; the ability of TYME and its collaborators to develop and realize collaborative synergies; competitive developments; and the factors described in the section captioned Risk Factors of TYMEs Annual Report on Form 10-K filed with the U.S. Securities and Exchange Commission on May 22, 2020, as well as subsequent reports we file from time to time with the U.S. Securities and Exchange Commission available at http://www.sec.gov.

The information contained in this press release is as of its release date and TYME assumes no obligation to update forward-looking statements contained in this release as a result of future events or developments.

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TYME Granted U.S. Patent Claims Covering Use of TYME-19 to Treat COVID-19 Infections - Business Wire

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AI Solving Real-world Problems and AI Ethics Among Top Trends for 2021, According to Oxylabs’ AI and ML Advisory Board – insideBIGDATA

Data science, machine learning, and AI experts highlight the top AI and ML trends they expect to shape the data science industry in 2021

The ongoing impact of Covid-19 is still affecting organizations nearly a year since the pandemic began, with business leaders continuing to leverage technology in order to navigate the crisis. According to Oxylabs dedicated AI and ML advisory board, some of the most important trends in 2021 will include the increased use of ethical AI for diversity, accountability, and model explainability, alongside increased instances of AI solving challenging real-world problems.

Oxylabs advisory board comprises the leading figures in the machine learning, AI, and data science industries and its members outline what they believe are the most important data science predictions for the year ahead:

Firstly, Pujaa Rajan, Machine Learning Engineer at Stripe, USA Ambassador at Women in AI andGoogleDeveloper MLExpert, believes COVID-19 will instigate a renewed enthusiasm for the application of edge AI in the healthcare industry and the use of ethical AI:

Covid-19 defined 2020 and although development in healthcare has historically been slower than other industries due to regulation this year will see a focus on edge AI in the healthcare industry and other industries. This will lead to the ability to run ML models locally, and tiny ML, resulting in smaller sized ML models that fit on smaller devices like phones. Businesses will focus on these specific, technical areas because they are related to data privacy and security, which the general public and government increasingly care about.Model explainability and interpretability is a space that the government, healthcare companies and finance companies are all actively exploring because of technical curiosity and business motivations. Many leaders will also finally prioritise AI ethics, diversity, inclusion, model explainability, and model interpretability after public outrage at many bad, biased, and unethical applications of AI. On the other hand, the biggest AI news last year was OpenAIs GPT-3, so I expect continued innovation in large NLP models. Software and hardware are like yin and yang. Since the larger models will need more efficient hardware, neural network accelerators will be a hot space.

Ali Chaudhry, PhD researcher, Artificial Intelligence atUCL, sees AI as having have more of a contribution in solving challenging real-world problems in 2021:

I think there will be more focus on fairness, transparency, accountability and explainability in AI systems this year, hence, we can expect more regulations from governments around the globe. We will also see AIs contribution in solving more challenging real-world problems, similar to the protein folding problem that was recently solved by AI. In terms of AI techniques that are set to emerge, there will be more real-world applications of Reinforcement Learning (RL) algorithms and RL will also retain its top position in academia.

Another prediction comes from Gautam Kedia, Machine Learning Engineering Manager at Stripe, ex-Applied Scientist Lead at Microsoft, previously Head of Applied ML at Lyft. He considers how AI-generated content could finally become mainstream across multiple sectors:

AI-generated content will become mainstream and in the next few years, I expect truly generative models to be producing logos, short stories, stock images, voiceovers and workouts, DALL-E is just a start and I believe this content will gradually start to pass the Turing Test. Self-driving cars will also take another step forward and I expect Waymo to start a taxi service directly competing with Uber & Lyft. Tesla will also release the much-awaited Full Self Driving computer.

Finally, Jonas Kubilius, AI researcher, Marie Skodowska-Curie Alumnus, and Co-Founder of Three Thirds is optimistic about the implementation of AI in healthcare but also has fears that AI investment may suffer:

Im certainly optimistic about AI-driven solutions making a greater impact in the healthcare sector and drug discovery, however, my only concern is the economic impact of the global COVID-19 pandemic. It may well be that there is a slowdown of investments in AI-driven solutions and research labs, forcing companies to justify any investments they make and focus very clearly on problems where AI brings a clear added value. With ever increasing pressure on governments and organisations to take action in regard to climate change, I expect to see more AI-driven solutions being leveraged in this field. Particularly in the areas that could benefit from the optimisation of manufacturing and logistics processes to reduce the impact they have on the environment.

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Global Transfection Reagent and Equipment Market: In-Depth Market Research and Trends Analysis till 2030 KSU | The Sentinel Newspaper – KSU | The…

The global transfection reagents and equipment market accounted for 1793.0 Million in 2020 and is estimated to be US$ 931.3 Million by 2029 and is anticipated to register a CAGR of 7.5%.

The report Global Transfection Reagent and Equipment Market, By Product (Reagents and Equipment), By Method (Biochemical Methods, Physical Methods, and Viral Methods), By Application (Biomedical Research, Gene Expression Studies, Cancer Research, Transgenic Models, Protein Production, and Therapeutic Delivery), By End User (Academics & Research Institutes and Pharmaceutical & Biotechnology Companies), and By Region (North America, Europe, Asia Pacific, Latin America, and the Middle East & Africa) Trends, Analysis and Forecast till 2029.

Get Sample Copy of This Report @ https://www.prophecymarketinsights.com/market_insight/Insight/request-sample/4486

Key Highlights:

In July 2020, Polyplus-transfection(R) SA, the leading biotechnology company that supports the gene and cell therapy market by supplying innovative transfection solutions, announced the launch of the industrys first GMP-compliant residual test for its PEIpro (R) product portfolio, transfection reagents designed for process development, pre-clinical, clinical and commercial lentivirus and adeno-associated virus (AAV) production for cell & gene therapies.In March 2020, Thermo Fisher, the largest maker of scientific tools, announced its plan to produce up to 5 million of a new test to detect the novel coronavirus that causes Covid-19.Analyst View:

Large number of application along with fast results

The cells which are transiently transfected takes around 24 96 hours post transfection and the mRNA is expressed within minutes after transfection that means it takes very less time to show the results. The main application of transfection reagent and equipment is used in studying the effect of gene expression, gene products, gene silencing and largely producing recombinant proteins. Highly increasing application like gene expression studies, protein production, transgenic models, therapeutic delivery, cancer research and biomedical research is expected to foster the transfection reagents and equipment market. Transfection of mammalian cells is mostly effective in the production of recombinant protein with proper folding and post-translational modification.

Technological advancements in transfection technology

The transfection reagents and equipment market has seen diverse technological achievements in equipment as well as reagents, to address the demands of biotechnology and researchers and biopharmaceutical associations. Increasing demand of synthetic genes, R & D investment and research activities, emerging economics drives the target market. For instance, in July 2020, Poly plus-transfection(R) SA, the leading biotechnology company that supports the gene and cell therapy market by supplying innovative transfection solutions, announced the launch of the industrys first GMP-compliant residual test for its PEIpro (R) product portfolio, transfection reagents designed for process development, pre-clinical, clinical and commercial lentivirus and adeno-associated virus (AAV) production for cell & gene therapies. Transfection promotes the process of introducing genetic material into eukaryotic cell to enable the production or expression of proteins using cells machinery.

Key Market Insights from the report:

The global transfection reagents and equipment market accounted for 1793.0 Million in 2020 and is estimated to be US$ 931.3 Million by 2029 and is anticipated to register a CAGR of 7.5%. The market report has been segmented on the basis of product, method, application, end user, and region.

Depending upon product, the reagents segment is projected to grow at highest CAGR over the forecast period. The reagents provide a well-structured overview of significant innovations, discoveries coupled with the technological advancements that occur in the global industry.Depending upon the method, the biochemical segment is projected to register highest share of the market in 2019. Biochemical segment has various applications in cell research, target validation and drug discovery, and technological advances such as synthetic genes, whose demand has been growing, thus anticipated to contribute to the growth of the market.In terms of application, cancer research projected to witness highest CAGR over the forecast period.By end-user, pharmaceutical & biotechnology companies segment estimated for highest share in 2019 due to strategic framework to boost the growth journey, actionable results to meet all the business priorities.By region, North America region contributes to the largest share in the global transfection reagents and equipment market due to due to rising prevalence of various cancers such as cervical cancer, breast cancer, colon cancer and prostate cancer. Further, the government of North America is highly increasing their investments in the biological research, genomics research and in the production of therapeutic proteins, which in turn boosts growth of the target market in this region.

To know the upcoming trends and insights prevalent in this market, click the link below:

https://www.prophecymarketinsights.com/market_insight/Global Transfection Reagents and Equipment Market-4486

Competitive Landscape:

The prominent player operating in the global transfection reagents and equipment market includes Thermo Fisher Scientific, Promega Corporation, Lonza, QIAGEN, F. Hoffmann-La Roche Ltd., and Bio-Rad Laboratories, Merck KGaA, OriGene Technologies, MaxCyte, Polyplus-transfection SA.

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Brightseed’s First Major Phytonutrient Discovery Finds Black Pepper May Help with Fatty Liver – The Spoon

Brightseed, which uses artificial intelligence (AI) to uncover previously hidden phytonutrients in plants, today announced preclinical data from its first major discovery targeting liver and metabolic health.

The discovery was made with Forager, Brightseeds AI platform that looks at plants on a molecular level to identify novel phytonutrient compounds (for example, antioxidants in blueberries). Once found, Forager then catalogs these compounds and uses that information to predict the health benefits of those compounds.

With todays announcement, Brightseeds Forager has identified phytonutrients that can help with fat accumulation in the pancreas and liver, a condition linked to obesity. Brightseed explained its findings in a press release, writing:

Using a computational approach with data from Brightseeds plant compound library, Forager identified two natural compounds with promising bioactive function, N-trans caffeoyltyramine (NTC) and N-trans-feruloyltyramine (NTF). Researchers determined that these compounds acted through a novel biological mechanism governing the accumulation and clearance of liver fat. The preclinical data was presented in the fall of 2020 as a poster session at The Liver Meeting Digital Experience hosted by American Association for the Study of Liver Diseases, and published as abstract #1679 in Hepatology: Vol 72, No S1.

The release continued:

IIn preclinical studies, NTC and NTF acted as potent HNF4a activators, promoting fat clearance from the steatotic livers of mice fed a high fat diet, by inducing lipophagy. HNF4a is a central metabolic regulator that is impaired by elevated levels of fat in the bloodstream resulting from chronic overeating. Administered in proper doses, NTC and NTF restored proper function of this central metabolic regulator, including maintaining healthy lipid and sugar levels in the bloodstream to normalize organ function. Their activities were confirmed using a cell-based human insulin promoter activation assay. Forager found NTC and NTF in over 80 common edible plant sources.

One of those plant sources, Brightseed Co-Founder and CEO, Jim Flatt told me by phone this week, is black pepper. Now, before you run out and grab your pepper grinder, there is still a lot of work that remains before the results of this discovery bear out.

First, the compounds still need to go through clinical trials to validate Brightseeds initial findings. This includes not only confirming any health benefits, but also determining the doses and best methods for administering the compounds. Then the best plant source for those compounds needs to be determined as well as the best method for compound extraction. Flatt told me that if all goes well, you can expect to see some form of supplement on the market by the end of 2022.

Even though that is a ways off, part of the reason to be excited by todays announcement is because of how little time it took Brightseed to make this particular discovery. Through its computational processes, Flatt told me his company was able to shrink what used to take years down to months. Fifteen to 20 percent of time that is computational saves us 80 percent of the time in the lab, Flatt said.

Brightseed has already analyzed roughly 700,000 compounds in the plant world for health properties and says its on track to surpass 10 million by 2025. Doing so could help unlock a number of previously unknown treatments for a number of ailments and conditions as well as general improvement to our metabolic and immuno health.

In addition to independent research such as todays findings, Brightseed also partners with major CPG brands to help them identify new applications for their products. For instance, Danone is using Brightseeds technology to help find new health benefits of soy.

Brightseeds announcement today also reinforces the bigger role AI will play in our food system. AI and machine learning is being used to do everything from turning data into cheese, to solving complex issues around protein folding.

As more discoveries using AI are made, more investment will be poured into the space, which will accelerate even more discoveries.

Related

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Brightseed's First Major Phytonutrient Discovery Finds Black Pepper May Help with Fatty Liver - The Spoon

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Gene expression signatures of target tissues in type 1 diabetes, lupus erythematosus, multiple sclerosis, and rheumatoid arthritis – Science Advances

Abstract

Autoimmune diseases are typically studied with a focus on the immune system, and less attention is paid to responses of target tissues exposed to the immune assault. We presently evaluated, based on available RNA sequencing data, whether inflammation induces similar molecular signatures at the target tissues in type 1 diabetes, systemic lupus erythematosus, multiple sclerosis, and rheumatoid arthritis. We identified confluent signatures, many related to interferon signaling, indicating pathways that may be targeted for therapy, and observed a high (>80%) expression of candidate genes for the different diseases at the target tissue level. These observations suggest that future research on autoimmune diseases should focus on both the immune system and the target tissues, and on their dialog. Discovering similar disease-specific signatures may allow the identification of key pathways that could be targeted for therapy, including the repurposing of drugs already in clinical use for other diseases.

The incidence of autoimmune diseases is increasing on a worldwide basis, and the prevalence of some of the most severe autoimmune diseases, i.e., type 1 diabetes (T1D), systemic lupus erythematosus (SLE), multiple sclerosis (MS), and rheumatoid arthritis (RA), has reached levels of prevalence ranging from 0.5 to 5% in different regions of the world (1). There is no cure for these autoimmune diseases, which are characterized by the activation of the immune system against self-antigens. This is, in most cases, orchestrated by autoreactive B and T cells that trigger and drive tissue destruction in the context of local inflammation (25). While the immune targets of T1D, SLE, MS, and RA are distinct, they share several similar elements, including common variants that pattern disease risk, local inflammation with contribution by innate immunity, and downstream mechanisms mediating target tissue damage. In addition, disease courses are characterized by periods of aggressive autoimmune assaults followed by periods of decreased inflammation and partial recovery of the affected tissues (3, 611). Endoplasmic reticulum stress (1215), reactive oxygen species (1619), and inflammatory cytokines, such as interleukin-1 (IL-1) and interferons (IFNs), are also shared mediators of tissue damage in these pathologies (2023).

Despite these common features, autoimmune disorders are traditionally studied independently and with a focus on the immune system rather than on the target tissues. There is increasing evidence that the target tissues of these diseases are not innocent bystanders of the autoimmune attack but participate in a deleterious dialog with the immune system that contributes to their own demise, as shown by our group and others in the case of T1D [reviewed in (3, 24, 25)]. Furthermore, in T1D, several of the risk genes for the disease seem to act at the target tissue levelin this case, pancreatic cellsregulating the responses to danger signals, the dialog with the immune system, and apoptosis (20, 25, 26). Against this background, we hypothesize that key inflammation-induced mechanisms, potentially shared between T1D, SLE, MS, and RA, may drive similar molecular signatures at the target tissue level. Discovering these similar (or, in some cases, divergent) disease-specific signatures may allow the identification of key pathways that could be targeted for therapy, including the repurposing of drugs already in clinical use for other diseases.

To test this hypothesis, we obtained RNA sequencing (RNA-seq) datasets from pancreatic cells from controls or individuals affected by T1D (27), from kidney cells from controls or individuals affects by SLE (28), from optic chiasm from controls or individuals affected by MS (29), and from joint tissue from controls or individuals affected by RA (30). In some cases, we also compared these datasets against our own datasets of cytokine-treated human cells (31). These unique data were mined to identify similar and dissimilar gene signatures and to search for drugs that may potentially revert the observed signatures. Furthermore, we searched for the expression of candidate genes for the different autoimmune diseases at the target tissue level and the signaling pathways downstream of these candidate genes.

These studies indicate major common gene expression changes at the target tissues of the four autoimmune disease evaluated, many of them downstream of types I and II IFNs, and massive expression of candidate genes (>80% in all cases). These findings support the importance of studying the target tissue of autoimmune diseases, in dialog with the immune system, to better understand the genetics and natural history of these devastating diseases.

The metadata of the tissue donors evaluated in the present analysis are shown in Table 1. The number of samples is proportional to the accessibility of the target tissues, with the highest number of samples available for joint tissue in RA. The age and sex of the patients reflect the natural history of the different diseases, with younger patients in the T1D group and a higher proportion of female patients in the MS and SLE groups. Sex information was obtained from the original metadata and, when not available, was inferred using chromosomal marker information present in the transcriptome (see Materials and Methods). Of note, while some of the samples used for RNA-seq were obtained following fluorescence-activated cell sorting (FACS) purification (e.g., pancreatic cells) (27), other samples comprised a mixture of target cells and infiltrating immune cells. Determination of the leukocyte marker CD45 expression in the different samples indicated a trend for higher presence of immune-derived cells among samples obtained in T1D, MS, and RA, but not in SLE (table S1). This contribution by immune cells was, however, modest. For instance, while in the cell preparation the number of transcripts per million (TPM) for CD45 in the patient group was 16.4 (mean), the TPM values for the following cell markers were as follows: INS (Insulin), 125.359; Sodium/potassium-transporting ATPase gamma chain (FXYD2a), 65; GCK (Glucokinase), 20; Homeobox protein Nkx-2.2 (NKX2-2), 28; Synaptotagmin 4 (SYT4), 36; Neurogenic Differenciation 1 (NEUROD1), 27; Homeobox protein Nkx-6.1 (NKX6-1), 27; and MAF BZIP Transcription Factor B (MAFB), 23, indicating that the observed responses are driven, at least in part, by the constitutive cells of the target tissues. Of note, proinflammatory cytokines decrease the expression of several of the cell markers (3, 20, 32) described above.

RNA-seq data from four studies of target tissues in autoimmune diseases were retrieved from the Gene Expression Omnibus (GEO) portal (https://ncbi.nlm.nih.gov/geo/), reanalyzed, and quantified with Salmon using GENCODE 31 as the reference. N/A, data nonavailable. For the sex column: M, male; F, female.

In the T1D and SLE datasets, but not in the MS and RA ones, there was a trend for more up-regulated than down-regulated genes in the target tissues, which was particularly marked in the T1D dataset, with more than twofold higher number of up-regulated genes as compared with the down-regulated ones (Fig. 1A). Of note, because of a statistically significant difference in the age of patients with RA and their respective controls, we have included age as an independent variable when determining the differentially expressed genes in the joint tissue samples (see Materials and Methods).

(A) Number of protein-coding genes differentially expressed in four autoimmune diseases. Each RNA-seq data set was quantified with Salmon using GENCODE 31 as the reference. Differential expression was assessed with DESeq2. The numbers within the bars represent the protein-coding genes with |fold change| >1.5 and an adjusted P value <0.05. RNA-seq sample numbers (n) are as follows: T1D (n = 4 for patients, n = 10 for controls), SLE (n = 20 for patients, n = 7 for controls), MS (n = 5 for patients, n = 5 for controls), and RA (n = 56 for patients, n = 28 for controls). Results for the RA samples were adjusted by age as an independent variable. (B to E) Gene set enrichment analysis (GSEA) of T1D (B), SLE (C), MS (D), and RA (E) target tissues. After quantification using Salmon and differential expression with DESeq2, genes were ranked according to their fold change. Then, the fGSEA algorithm (76) was used along with the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases to determine significantly modified pathways. Bars in red and blue represent, respectively, a positive and negative enrichment in the associated pathway. The x axis shows the normalized enrichment score (NES) of the fGSEA analysis, and the y axis the enriched pathways. The numbers at the end of the signaling pathway names represent, respectively, (i) the number of genes present in the leading edge of the GSEA analysis and (ii) the total number of genes present in the gene set.

Enrichment analysis of these disease-modified genes (Fig. 1, B to E) indicated similarities and differences between the different autoimmune diseases. Thus, both T1D and SLE have several up-regulated IFN-related pathways among the top up-regulated ones (Fig. 1, B and C); IFN pathways were also detected as enriched for MS and RA, but not among the 20 top ones [e.g., MS: IFN- signaling normalized enrichment score (NES) = 2.26 (P adj. < 0.007); RA, IFN- signaling NES = 2.64 (P adj. < 0.004)]. This similar enrichment in IFN-related genes can also explain the appearance of SLE as the top up-regulated pathway in T1D (Fig. 1B). Up-regulated pathways related to antigen presentation or antigen-related activation of immune cells were present for the four diseases (Fig. 1, B to E), in line with their autoimmune nature, while complement cascades were preeminent in MS (Fig. 1D) and RA (Fig. 1E), but less so in T1D and SLE. To evaluate whether these observed IFN-induced signatures originate, at least in part, from nonimmune cells in the target tissues, we reanalyzed available single-cell(sc)/nucleus(sn)RNA-seq data focusing on nonimmune cells in affected tissues in T1D [pancreatic cells (33)], SLE [kidney epithelial cells (34)], MS [brain neurons (35)], and RA [synovial fibroblasts (36)] (fig. S1A), confirming that there is a significant IFN signature in the target of the four autoimmune diseases as measured by an IFN response score, defined as the average expression of known IFN-stimulated genes (ISGs; see Materials and Methods) (34, 37).

The down-regulated pathways tended to be more disease specific and related to the dysfunction of the target organ. Thus, for T1D, there was down-regulation of pathways involved in integration of energy metabolism, a key step for insulin release, and in regulation of gene expression in cells, which reflects the down-regulation of several transcription factors (TFs) critical for the maintenance of cell phenotype and function (e.g., PDX1 and MAFA) (38) (Fig. 1B), while in RA, there was a decrease in collagen chain trimerization, an important step for proper collagen folding (Fig. 1E) (39). Moreover, down-regulation of pathways involved in lipid metabolism was enriched in MS samples (Fig. 1D). Supporting that, disruption of lipid metabolism in oligodendrocytes compromises the lipid-rich myelin production/regeneration, a hallmark of MS, both in in vitro studies (40) and in samples obtained from individuals with MS (41).

Gene set enrichment analysis (GSEA) of the sc/snRNA-seq data of nonimmune cells from the four autoimmune diseases (fig. S1, B to E) confirmed several up-regulated pathways in common, including IFN signaling (present for all diseases, although not always among the top 20 shown), T1D (which appears in three of the four diseases), allograft rejection, etc. As observed in the bulk RNA-seq analysis, there were less similarities between diseases regarding the down-regulated pathways.

We also analyzed the intersection between significantly up- and down-regulated genes of the bulk RNA-seq of the four diseases using another criterion, namely, considering genes as significantly modified if they presented a false discovery rate <0.10 without a fold change threshold (fig. S2, A and B). This showed a higher similarity among up- than down-regulated genes, but there were few genes in common between the four diseases. On the basis of a hypergeometric test to search for gene set enrichment for the cases where there were >50 genes in common between two and three diseases, we identified IFN signaling, antigen processing, and presentation and cytokine signaling, among others. It was, however, difficult to find common pathways among the down-regulated genes. A limitation of this approach is that we can only analyze genes that pass a fixed statistical cutoff, which makes the results very susceptible to the number of samples studied, as presently observed for the higher intersection between RA (a disease with a much higher number of samples) and the other autoimmune diseases. This type of analysis must thus be redone as more samples become available for the different diseases.

To obtain more detailed information on the (dis)similarities between the different autoimmune diseases, avoiding the pitfalls mentioned above for threshold-based analysis, we performed the rank-rank hypergeometric overlap (RRHO) analysis (Fig. 2) (42), a genome-wide approach that compares two equally ranked datasets using a threshold-free algorithm (see Materials and Methods). The main similarities between the diseases were observed among up-regulated genes, while there was no major intersection of commonly down-regulated genes between datasets (Fig. 2). This finding is in line with the above-described observation that down-regulated genes tended to be target-tissue related (Fig. 1, B to E). cells in T1D, in particular, showed a strong correlation with regard to up-regulated genes with SLE, RA, and MS (Fig. 2). The functional enrichment analysis of these up-regulated overlapping pathways showed concordance for both types I and II IFN signaling for nearly all disease pairs (Fig. 3). Pathways related to neutrophil degranulation were highly up-regulated when comparing MS against T1D (Fig. 3B), SLE (Fig. 3D), or RA (Fig. 3F); this pathway also appeared highly in common between T1D and RA (Fig. 3C).

(A) Genes were ranked by their fold change from the most down- to the most up-regulated ones and then submitted to the RRHO algorithm. The level map colors display the adjusted log P values of the overlap (the P values were adjusted using the Benjamini and Yekutieli method) between genes up-regulated in both datasets (bottom left quadrant), down-regulated in both (top right quadrant), up-regulated in the left-hand pathology and down in the bottom part (top left quadrant), and down in the left-hand pathology and up-regulated in the bottom part pathology (bottom right quadrant). (B) The panel displays the number of genes significantly overlapping in each pairwise analysis (A). NS, not significant quadrant.

(A to F) Genes significantly overlapping between different pairs of autoimmune diseases in the RRHO analysis (Fig. 2B) were selected for enrichment analysis using the clusterProfiler tool with the Reactome database. The top 20 gene sets are represented according to their adjusted P values (Benjamini and Hochberg correction) and their gene ratio (no. of modified genes/total gene set size). Diseases were analyzed in pairs. Enrichment analysis of genes significantly up-regulated in the target tissues of both (A) T1D and SLE, (B) T1D and MS, (C) T1D and RA, (D) SLE and MS, (E) SLE and RA, and (F) MS and RA.

We next investigated the potential TFs controlling the observed interdisease similarities. For this purpose, we evaluated the enrichment of TF binding site motifs in the promoter region of up-regulated genes from the pairwise analysis of autoimmune diseases (fig. S3). In line with the predominance of IFN-related pathways observed in Fig. 3, there was a high prevalence of common binding site motifs for IFN-induced TFs, including IFN-stimulated response element (ISRE), IFN regulatory factor 1 (IRF1), and IRF2, particularly when comparing T1D versus SLE (fig. S3A) and T1D versus RA (fig. S3C). To examine whether these TFs are expressed by constitutive cells of the target tissues, we have reevaluated the TF expression in nonimmune cells present in sc/snRNA-seq of the target tissues from the four autoimmune diseases. Since the presently available methods for sc/snRNA-seq only detect on average 1000 to 5000 genes per cell (43), which is 75 to 80% lower than the total number of genes identified by bulk cell RNA-seq (>20,000 genes), we selected for this analysis the top 10 TFs presenting the highest expression in the affected target tissues. By this approach, we observed that the majority of these TFs are expressed by nonimmune cells from the target tissues (fig. S3G). In agreement with this observation, we have previously shown that exposure of the human cell line EndoC-H1 to INF leads to the activation of several of the same TFs identified, including signal transducer and activator of transcription 1 (STAT1), STAT2, STAT3, IRF1, and IRF9 (31, 48).

To assess whether a putative in vivo type I IFN signaling in the context of different autoimmune diseases activates similar pathways in the target tissues, we compared gene expression of primary human islets (31) and skin keratinocytes (44) exposed in vitro to IFN- for 8 and 6 hours, respectively (fig. S4). There were approximately 40% differentially expressed genes in common between these two tissues (fig. S4A), leading to the induction of pathways such as IFN signaling and antigen presentation/processing (fig. S4B) that were similar to the pathways observed in target tissues from patients affected by T1D (Fig. 1B and fig. S5) or SLE (Fig. 1C and fig. S5).

It is noteworthy that when comparing SLE versus T1D and SLE versus RA (Fig. 2, A and B), there were a large number of genes up-regulated in one disease but down-regulated in the other. A more detailed analysis of these oppositely regulated genes (fig. S6) indicated that neutrophil degranulation and signaling by RHO GTPases (guanosine triphosphatases) were among the most enriched gene sets. A similar observation was made regarding SLE versus RA, where neutrophil degranulation was also the most represented gene set. This apparent disagreement between genes regulating neutrophil degranulation in SLE and other autoimmune diseases may reflect the presence of two distinct populations of neutrophils in patients with SLE that have functional differences in pathways controlling chemotaxis, phagocytosis, and degranulation (45). Other dissimilarities include the anti-inflammatory IL-10 signaling and groups related to the regulation of the dialog between immune and resident cells, such as immunoregulatory interaction between a lymphoid and nonlymphoid cell and PD-1 (programmed cell death protein 1) signaling.

The availability of the above-described datasets allowed us to mine the overlapping genes in the target tissues of the different autoimmune diseases to search for common therapeutic targets, with the potential to find drugs to be repurposed (Fig. 4). As a proof of concept, we identified dihydrofolate reductase inhibitors as a potential therapeutic target for several pairs of autoimmune diseases (Fig. 4, B to D and F), and methotrexate, a member of this class, is already routinely used for the treatment of different autoimmune diseases, including RA (46) and SLE (47). Bromodomain inhibitors were also observed as common perturbagens between T1D and SLE (Fig. 4A) and SLE versus RA (Fig. 4E). This is in line with our recent observations that two of these bromodomain inhibitors, JQ1 and I-BET-151, protect human cells against the deleterious effects of IFN- (31). There were additional interesting candidates, some with a profile covering multiple diseases, such as phosphoinositide 3-kinase (PI3K) (T1D versus SLE, SLE versus RA, and MS versus RA) and janus kinase (JAK) inhibitors (SLE versus RA and MS versus RA), while others acting on specific pairs of diseases, namely, bile acids (T1D versus MS) and fibroblast growth factor receptor (FGFR) inhibitors (SLE versus MS) (Fig. 4). Of note, clinical trials are currently evaluating the effects of the bile acid tauroursodeoxycholic acid (TUDCA) in patients with recent-onset T1D (ClinicalTrials.gov, NCT02218619) and MS (ClinicalTrials.gov, NCT03423121).

(A to F) After determining statistically which genes were overlapped in pairs of autoimmune diseases from the RRHO analysis (Fig. 2), the top 150 overlapping genes were submitted to the Connectivity Map database to identify perturbagen classes driving an opposite signature (negative tau score) to the one present in the target tissues of the four autoimmune diseases. Only classes with a median tau score <80 were considered. (A to F) Perturbagen classes driving down the genomic signatures of up-regulated genes. The same methodology and conditions have been applied for subsequent analysis: (A) T1D and SLE, (B) T1D and MS, (C) T1D and RA, (D) SLE and MS, (E) SLE and RA, and (F) MS and RA. EGFR, epidermal growth factor receptor. LOF, Loss of Function; GOS, Gain of Function; IAP, inhibitor of Apoptosis; FGFR, Fibroblast Growth Factor Receptor; MDM, Murine Double Minute; HIF, Hypoxia Inducible Factor; BCL, B-Cell Lymphoma.

We have previously shown that isolated human pancreatic islets express a large number of risk genes for T1D (20, 24, 26, 48), and we presently examined whether this is also the case for the target tissues in other autoimmune diseases (table S2). Confirming our previous findings, 81% of risk genes for T1D were expressed in human cells; similar findings were observed for the target tissues for SLE (92%), MS (83%), and RA (88%). The autoimmune assault changed the expression of >65% of these candidate genes for joint tissue RA (table S2), but the number of disease-induced and significantly modified genes was much smaller for the other autoimmune diseases, probably because of limited statistical power associated to the number of samples analyzed (>80 samples studied in the case of RA and between 10 and 27 for the other diseases). The list of risk genes expressed in the target tissues is available in data file S1. An overview of these candidate genes and their coexpression in different autoimmune diseases is provided in Fig. 5. Genes related to antigen presentation [human lymphocyte antigen (HLA)DQB1 and HLA-DRB1] and to type I IFN signaling (TYK2) are present in all target tissues for the four autoimmune diseases. Reactome (49) analysis of risk genes in T1D (data file S2) identified ILs and IFN signaling as important pathways. IFN signaling also appears pro-eminently for kidney tissue in SLE, optic chiasm in MS, and joint tissue in RA (data file S2), but there are also clusters related to defense against the autoimmune assault, including PD-1 (for all diseases) and IL-10 signaling (for SLE and MS only); PD-1PDL1 (programmed death ligand 1) is probably also an important defense mechanism of human cells in T1D (50).

Venn diagram representing risk genes identified in GWAS studies in target tissues for T1D, SLE, MS, and RA. For each disease, the risk genes were extracted from the GWAS Catalog (www.ebi.ac.uk/gwas/) and selected as described in Materials and Methods. In brief, each list was curated according to their relationship to the disease, and only genes with a P value <0.5 108 for their SNP-trait relationship were kept. Last, an intersection between the four lists was performed and represented as a Venn diagram. Numbers in the diagram represent the numbers of genes present in each subgroup, and genes overlapping between diseases are displayed by their HGNC symbols. A gene was considered as expressed if it presents a mean TPM > 0.5 in either the patient or control group. N/A, not applicable (no gene in common).

To evaluate whether the observed candidate genes are expressed in nonimmune cells from the target tissues studied, we have used a similar approach as done for the TF analysis (fig. S3G) and revised sc/snRNA-seq data from nonimmune cells in affected tissues in T1D (33), SLE [kidney epithelial cells (34)], MS [brain neurons (35)], and RA [synovial fibroblasts (36)]. This confirmed that >80% of the top 50 risk genes are expressed by the target cells (fig. S1, F to I). Of note, the present limitations of the scRNA-seq method regarding the number of genes detected (commented upon above) may explain why less candidate genes are observed in single cells (fig. S1, F to I) than in whole tissue or FACS-sorted bulk cells (data file S1).

In the present study, we tested the hypothesis that target tissues from four different autoimmune diseases, namely, T1D, SLE, MS, and RA, engage in a dialog with the invading immune cells that leaves molecular footprints. These footprints may share similarities, as local inflammation is a common outcome of these diseases, and point to common mechanisms that can be targeted by therapy.

The analysis of the gene expression patterns of the target tissues in the different diseases showed up-regulation of type I and II IFNrelated pathways, which is in line with observations made in the peripheral blood of individuals with T1D (51), SLE (52, 53), MS (54), and RA (55). These descriptive similarities were confirmed by comparing the ranking of the up-regulated genes via RRHO, a method that allows the comparison between differentially expressed genes in control and diseased tissue from two different diseases, outlining the similarities and/or dissimilarities between the modified genes in both diseases. Here, we observed clear but different degrees of overlap between the diseases mostly regarding the up-regulated expression patterns. In support of the robustness of the present findings, these similarities were present despite the fact that the original RNA-seq data were obtained by different research teams, using different extraction and sequencing processes, and that there were major differences between the studies regarding age and sex of the patients and respective controls (many of these differences were inherent to the diseases studied, e.g., SLE is more common in females).

The observed similarities in pathway activation between target tissues were translated into the identification of several classes of drugs that could potentially be used to treat more than one autoimmune disease (Fig. 4). Among them, JAK inhibitors, which act downstream of the types I and II IFN receptors by blocking activation of the kinases JAK1 and JAK2, are of particular interest. These inhibitors were recently approved for the treatment of RA (56) and had promising results in a phase 2 clinical trial of patients with SLE (57). In line with this, JAK inhibitors prevent the proinflammatory and proapoptotic effects of IFN- on human pancreatic cells (31) and revert established insulitis in diabetes-prone NOD (nonobese diabetic) mice (58). Another class of drugs presently identified for potential use in several autoimmune diseases are the PI3K inhibitors. These drugs target a family of lipid kinases that phosphorylate phosphoinositides from cell membranes, modulating cellular processes such as cell growth, metabolism, and immune responses. In agreement with our analysis, inhibitors of the PI3K isoforms and have beneficial effects in animal models of MS (59), SLE (60), and RA (61). PI3K inhibitors, however, may have opposite effects on different tissues. Thus, PI3K inhibitors exacerbate inflammatory responses in the airways and gut, tissues often exposed to pathogens, leading to severe cases of pneumonitis and colitis (62). This indicates that selection of potential new therapeutic agents needs to consider also the specific characteristics of the target tissue(s). This is in agreement with our present observations of tissue-specific down-regulated pathways in different diseases, such as pathways related to maintenance of the cell phenotype in T1D, or down-regulation of pathways involved in collagen folding in joint tissues from RA.

There have been previous attempts to perform individual drug repurposing on these pathologies [e.g., (63, 64)]. Our present study attempts to expand this approach, potentially leading to drug repurposing for multiple autoimmune diseases, for instance, in the case of JAK inhibitors. Repurposing already-studied drugs provides the benefits of having their pharmacodynamic and pharmacokinetic profiles already well studied, which considerably reduces the bench-to-bedside time frame (65), and helping the treating physicians to survey for previously detected side effects.

More than 80% of candidate genes for which a single-nucleotide polymorphism (SNP)trait link has been deemed significant are expressed in the target tissues of the different autoimmune diseases studied. This is in line with our previous observations in T1D (20, 26, 48), where these candidate genes probably regulate cell responses to danger signals, such as viral infections, and the signal transduction of type I IFNs (23). The fact that similar observations are now made in the target tissues of SLE, MS, and RA (present data) suggests that future studies in these diseases should also consider the impact of candidate genes acting at the target tissue level. Of note, and to detect eQTL (Expression quantitative trait loci) in target tissues, it may be necessary to expose them to relevant stimuli, such as proinflammatory cytokines in the case of T1D (26).

The present observations, showing the expression of candidate genes in the target tissues of autoimmune diseases, may contribute to explain why certain people have different innate immune responses at the tissue level to seemingly similar triggers (such as viral infections or other danger signals), leading to different outcomes, e.g., progressive tissue damage or resolution of inflammation and return to homeostasis. For instance, diverse polymorphisms in candidate genes for T1D may contribute to disease at the cell level by regulating antiviral responses, innate immunity, activation of apoptosis, and, at least for a few of them, cell phenotype (24, 25, 66).

The candidate genes presently observed as overlapping between target tissues of two or more diseases are mostly related to inflammatory mediators, particularly the signal transduction of IFNs, suggesting that similarities between these diseases are dependent, at least in part, on the genetically mediated regulation of local immune responses. These findings may have therapeutic implications. For instance, one of the candidate genes in common between all the four autoimmune diseases is TYK2, a key component of the JAK-STAT signaling pathway. TYK2 inhibitors are already in phase 3 clinical trial for another autoimmune disease, psoriasis (67), and two different TYK inhibitors protect human cells against the deleterious effects of IFN- (68). Targeting IFN pathways at an early step of its signal transduction may not be, however, a sufficiently specific approach, and the role of IFNs may vary according to the stage of disease and the genetic background of the affected individuals. The success of IFN-blocking therapies in human SLE and other rheumatic diseases remains to be proven (69).

The data generated in the present study contribute to a better understanding of the communication between the immune system and the target tissues in T1D, SLE, MS, and RA, and strengthen the putative implication of the target tissues in these autoimmune diseases. These findings also indicate a role for similar candidate genes expressed in target tissues of two or more diseases and indicate potential new therapeutic agents to target key similar pathways. As a whole, these observations suggest that future research on the genetics and pathogenesis of autoimmune diseases should focus on both the immune system and their target tissues and on their dialog.

The studys first limitation relates to the scarcity of RNA-seq data for target tissues in autoimmune diseases, particularly in the cases where these tissues are difficult to access, such as in T1D or MS. This decreases the power of the analysis and may bias the data in favor of diseases where a larger number of samples were available (e.g., RA). Another issue is the stage of the disease, as the impact of the immune system on the target tissues may differ in the early and late phases of the disease [for instance, in the case of T1D, innate rather than adaptive immunity may have a major role at earlier stages (3, 25, 70)]. Unfortunately, and because of the scarcity of samples in, for instance, T1D or MS, this stage issue is difficult to address. It is noteworthy that despite these limitations, it was still possible to obtain clear conclusions from the available data.

Another potential limitation is that immune cells are present in the target tissue preparations analyzed (although there was a statistically significant increase in the expression of the immune marker CD45 only in T1D and RA), which may affect the gene expression pathways described above. The facts that (i) an IFN signature is present in nonimmune cells of the diseased tissues analyzed and these nonimmune cells express several candidate genes for the diseases studied (fig. S1); (ii) at least in the case of a pure human cell line, EndoC-H1 cells, exposure to IFN- induces a gene signature that is similar to that observed in cells obtained from patients affected by T1D (31); and (iii) histological analysis of pancreatic islets from patients with T1D show expression of HLA class I (ABC) (71), HLA-E (31), PDL1 (50), CXCL10 (72), and STAT1 (71) in pancreatic cells, taken as a whole, suggest that at least part of the observed gene signatures originate from the target tissues and cannot be explained by the immune infiltration alone. Future follow-up studies based on direct histological staining of the specific cells involved are required to define the exact contribution of immune and nonimmune cells in the affected target tissues.

For each dataset, control and patient target tissue gene expressions were quantified using Salmon version 0.13.2 (73) with parameters --seqBias gcBias --validateMappings. GENCODE version 31 (GRCh38) (74) was chosen as the reference genome and has been indexed with the default k-mer values. Differential expression was performed with DESeq2 version 1.24.0 (75). For each gene included in DESeq2s model, a log2 fold change was computed and a Wald test statistic was assessed with a P value and an adjusted P value. In this study, we consider a gene as differentially expressed when |fold change| >1.50 and adjusted P value <0.05. Since there was a statistical difference in the age between patients with RA and controls, for this particular dataset, we have taken age as an independent variable in the general linear model performed by DESeq2. To introduce age as a confounding factor in the analysis, we performed a binning on the ages and assigned each donor a group, respectively: 10 to 29, 30 to 49, 50 to 69, and >70 years old. All the other parameters of the DESeq2 analysis were the same as for the others target tissues.

We have obtained the expression matrices containing the processed reads from transcriptome studies of the following target tissues: (i) scRNA-seq from cryo-banked islets obtained from three donors with T1D and three controls matched for body mass index, age, sex, and storage time, performed using the SmartSeq-2 protocol as described in (33) and accessible under the Gene Expression Omnibus (GEO) number GSE124742; (ii) scRNA-seq from kidney biopsies from 24 patients with LN and 10 control samples acquired from living donor kidney biopsies using a modified CEL-Seq2 protocol as described in (34) and accessible in the ImmPort repository (accession code SDY997); (iii) scRNA-seq from snap-frozen brain tissue blocks obtained at autopsies from 10 patients with MS (1 primary progressive MS, 9 secondary progressive MS) and 9 nonaffected individuals processed using the 10x Genomics Single-Cell 3 system as described in (35) and accessible on Sequence Read Archive (SRA; accession number PRJNA544731); and (iv) scRNA-seq of synovial tissues from ultrasound-guided biopsies or joint replacements of 36 patients with RA and 15 patients with osteoarthritis, as reference controls, using the CEL-Seq2 protocol as described in (34) and available at ImmPort (accession code SDY998). After that, we normalized the gene expression levels by transforming the counts to log2(CPM + 1) (counts per million).

For the purpose of reproducibility, we have kept the same cell identity classification defined in the original sc/snRNA-seq study (3336). To represent nonimmune cells on the target tissues, we have selected (i) in T1D, the cells isolated from pancreatic islets; (ii) in SLE, all the kidney epithelial cells from the kidney biopsy; (iii) in MS, all the cells from different clusters of brain neurons; and (iv) in RA, all the cells from the fibroblast clusters of joint synovial tissues.

For most, but not all, target tissues, sex information was available in the metadata on the GEO website. To compensate for this lack of information, we inferred the sex based on the expression of 40 genes exclusively coded on the Y chromosome and the female-expressed XIST (X-inactive specific transcript) (data file S1). We created a machine learning model on the basis of a linear discriminant analysis algorithm that we trained on the expression of both controls and patient expression matrices in RA. The training was supervised with the sex described in the metadata as the desired outcome. We then tried our model to predict the sex of patients on different target tissues (i.e., T1D and MS) where the outcome was known, according to their metadata, which provided only one prediction different from the expected outcome (96% accuracy). This allowed us to estimate the sex ratio in the studies missing this information in the available metadata.

Risk genes associated with each disease were identified using genome-wide association study (GWAS) catalog (www.ebi.ac.uk/gwas/; consulted January 2020). The candidate genes were selected on the basis of the following criteria: (i) T1D, SLE, MA, and RA as the disease/trait evaluated by the study; (ii) a P value of <0.5 108 for the lead SNP; (iii) selecting the reported genes linked to the lead SNP described by the original study; and (iv) expression of the reported genes in the target tissue (TPM > 0.5). An overlap between the four lists of genes was then performed and represented as a Venn diagram.

To evaluate for the presence of types I and II IFN signatures on the target tissues of the four autoimmune diseases, we have calculated for each cell from the sc/snRNA-seq an ISG score. This ISG score was calculated as the average expression of known ISGs listed on data file S1. The statistical difference between groups was determined using a two-tailed Mann-Whitney U test.

To compare the genomic signatures of the target tissues of the four autoimmune diseases, we used an RRHO (42) mapping, an unbiased method to uncover the concordances and discordances between two similarly ranked lists. Briefly, for a pair of diseases, the full list of genes is ranked according to their fold change from the most down-regulated to the most up-regulated gene. Then, an intersection of shared genes is performed, and the analysis of the ranking order of genes is performed with a hypergeometric test.

The visual output of this analysis is an RRHO level map (Fig. 2A), where the hypergeometric P value for enrichment of k overlapping genes is calculated for all possible threshold pairs for each experiment, generating a matrix where the indices are the current rank in each experiment. P values for each test are then log transformed and reported on a heatmap to display the degrees of similarities according to four quadrants representing the concordance or the discordance in gene ranking in the two differential expression analysis (e.g., up-regulated in one disease and down-regulated in the other).

The functional enrichment analysis was based on results from the differential expression analysis. Genes from bulk RNA-seq data were preranked according to the Wald test statistic of the differential expression results from DESeq2. For sc/snRNA-seq data, we filtered out genes that were expressed in less than 10% of all cells to minimize the dropout impact on the overall gene expression. The remaining genes were then preranked according to the log2 fold change of the differential expression results from DESeq2. We used fGSEA (76) along with the Kyoto Encyclopedia of Genes and Genomes (KEGG) (77) and Reactome (49) databases as the references to determine which pathways were positively or negatively enriched in the target tissue of each disease. Default parameters were used, except for the number of permutations (10,000) for the most accurate P values. For bulk RNA-seq data, results with an adjusted P value <0.05 (Benjamini-Hochberg correction) were then sorted according to their NES. For sc/snRNA-seq data, results with an adjusted P value <0.15 (Benjamini-Hochberg correction) were then sorted according to their NES.

To determine the functional enrichment in genes up-regulated in pairs of diseases, we used a hypergeometric test included in the clusterProfiler package (78) on the genes overlapping significantly in the RRHO mapping. The Reactome (49) database was used as the reference for the gene sets. Default parameters were used, and P values were adjusted with the Benjamini-Hochberg correction.

Genes differentially expressed with an adjusted P value <0.10 (Benjamini-Hochberg correction) were selected. The gene lists of all diseases were then overlapped and represented as a Venn diagram of up- or down-regulated genes. In case of an overlap of >50 genes, the gene list was processed using a hypergeometric test with the Reactome database as the reference. Defaults parameters were used, and P values were adjusted with the Benjamini-Hochberg correction.

Motif discovery for TF binding site in the promoter regions of up-regulated genes was done using the script findMotifs.pl from the HOMER (79) tools suite with parameters -start -2000 -end 2000. The promoter regions were considered as 2000 base pairs from the gene transcription start site. Known TF binding site motifs uncovered and included in the study have a P value <0.05.

For each RRHO analysis result, we picked the top 150 up-regulated genes shared between two diseases and processed this list with the Connectivity Map dataset (80) using the cloud-based CLUE software platform (https://clue.io). This allowed us to query the database for compounds that are driving down the input genomic signatures, revealing potential drugs that could be repurposed to treat one or more diseases. We focused then on perturbagen classes that displayed a negative median tau score and retained as potential drug candidates only classes with a median tau score <80.

TPM values are given according to their means SD. Results considered as significant in this study have a P value (or an adjusted P value when applicable) <0.05. For gene expression, we considered that a gene is differentially expressed if |fold change| >1.5 and adjusted P value <0.05, unless explicitly stated.

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Gene expression signatures of target tissues in type 1 diabetes, lupus erythematosus, multiple sclerosis, and rheumatoid arthritis - Science Advances

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Comparative modelling unravels the structural features of eukaryotic TCTP implicated in its multifunctional properties: an in silico approach -…

This article was originally published here

J Mol Model. 2021 Jan 7;27(2):20. doi: 10.1007/s00894-020-04630-y.

ABSTRACT

Comparative modelling helps compare the structure and functions of a given protein, to track the path of its origin and evolution and also guide in structure-based drug discovery. Presently, this has been applied for modelling the tertiary structure of highly conserved eukaryotic TCTP (translationally controlled tumour protein) which is involved in a plethora of functions during growth and development and also acts as a biomarker for many cancers like lung, breast, and prostate cancer. The modelled TCTP structures of different organisms belonging to the eukaryotic group showed similar spatial arrangement of structural units except loops and similar patterns of root mean square deviation (RMSD), root mean square fluctuation, and radius of gyration (Rg) inspected through molecular dynamics simulations. Essential dynamics (ED) analyses revealed different domains that exhibited different motions for the assistance in its multifunctional properties. Construction of a free-energy landscape (FEL) based on Rg versus RMSD was employed to characterize the folding behaviours of structures and observe that all proteins had nearly similar conformation and topologies, indicating common thermodynamic/kinetic pathways. A physico-chemical interaction study demonstrated the helices and sheets were well stabilized with ample amounts of bonding compared to turns or loops and charged residues were more accessible to solvent molecules. Hence, the current study reveals the important structural features of TCTP that aid in diverse functions in a wide range of organisms, thus extending our knowledge of TCTP and also providing a venue for designing the potent inhibitors against it.

PMID:33410974 | DOI:10.1007/s00894-020-04630-y

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Comparative modelling unravels the structural features of eukaryotic TCTP implicated in its multifunctional properties: an in silico approach -...

Recommendation and review posted by Alexandra Lee Anderson


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