ASCC1 Human

What is ASCC1 and what are its core domains?

ASCC1 uniquely combines two evolutionarily ancient domains: a nucleotide-binding K-Homology (KH) domain associated with regulating splicing, transcription, and translation functions, and a two-histidine phosphodiesterase (PDE) domain associated with hydrolysis of cyclic nucleotide phosphate bonds. This combination is critical for ASCC1's dual functionality in nucleic acid binding and processing. Crystal structures have revealed high-resolution details of these domains conserved over 500 million years of evolution .

What genetic disorders are associated with ASCC1 mutations?

Bi-allelic variants in ASCC1 cause the ultrarare bone fragility disorder "spinal muscular atrophy with congenital bone fractures-2" (SMABF2). Affected individuals exhibit severe muscular hypotonia, inability to breathe and swallow, virtual absence of spontaneous movements, progressive brain atrophy, gracile long bones, very slender ribs, and bone fractures. The typical cause of death is respiratory failure from severe muscle hypotonia, usually occurring within the first few months of life .

How is ASCC1 involved in cellular differentiation pathways?

ASCC1 plays a crucial role in directing mesenchymal stromal cell (MSC) fate. Knockdown experiments demonstrate that ASCC1 deficiency inhibits osteoblastogenesis while promoting adipogenesis. This is evidenced by downregulation of osteogenic markers (ALPL, RUNX2, CTNNB1) and upregulation of adipogenic markers (FASN, PPARγ) in ASCC1-deficient cells. This cellular phenotype explains the bone fragility observed in SMABF2 patients, who demonstrate high bone marrow adiposity, decreased osteogenesis, and bone fragility .

What does the crystal structure reveal about ASCC1's functional mechanisms?

The crystal structures of human (Hs) and Alvinella pompejana (Ap) ASCC1 have identified several key structural elements:

• A novel Helix-Clasp-Helix (HCH) nucleotide binding motif in the KH domain

• A V-shaped PDE nucleotide binding channel with two His-Φ-Ser/Thr-Φ (HXT) motifs positioned for catalysis

• An atypical active-site histidine torsion angle in the second HXT motif, suggesting a novel PDE substrate

• A flexible active site loop (ActSite-loop) that coordinates with catalytic residues, potentially having a regulatory effect

• Small-angle X-ray scattering (SAXS) showing aligned KH-PDE RNA binding sites with limited flexibility in solution

How does ASCC1 demonstrate RNA binding specificity?

ASCC1 exhibits sequence-specific binding to CGCG-containing RNA through its KH domain. This specificity is determined by key amino acids:

• Tyr75 mandates an amide from cytosine or adenine for base 1

• His100 stacks on base 2, requiring a purine (adenine or guanine)

• Lys84 selects for a pyrimidine with a carbonyl at base 3

• A main chain carbonyl (Ile97) selects cytosine over uracil

• Base 4 likely requires a purine for optimal stacking

This specificity was confirmed experimentally through electrophoretic mobility shift analysis (EMSA) under stringent 150 mM KCl conditions, showing binding specifically to RNA containing the CGCG sequence .

What signaling pathways does ASCC1 interact with in bone development?

ASCC1 influences bone development through multiple signaling pathways:

• TGF-β/SMAD signaling: Mutant ASCC1 exerts an inhibitory effect on this pathway, which is critical for bone development

• RUNX2 regulation: ASCC1 deficiency downregulates RUNX2, the master regulator of osteoblastogenesis

• SERPINF1 modulation: Mutant ASCC1 downregulates SERPINF1, which is involved in osteoblast and adipocyte differentiation

• PPARγ pathway: ASCC1 knockdown promotes adipogenesis via increased PPARγ expression

• Wnt/β-catenin pathway: ASCC1 deficiency reduces CTNNB1 (β-catenin) expression during osteogenic differentiation

How can researchers effectively model ASCC1 deficiency in cellular systems?

To model ASCC1 deficiency effectively:

• shRNA knockdown approach:

  • Use at least two separate shRNAs targeting ASCC1 to ensure reproducibility

  • Verify knockdown efficiency (60-70% reduction) through qPCR at multiple timepoints

  • Confirm protein reduction through proteomics or Western blotting

  • Maintain knockdown throughout experimental timeline for long-term studies (17-25 days for differentiation studies)

• PCR validation methods:

  • For confirming mutations in patient-derived cells, use primers flanking deletion sites (e.g., ASCC1-del-D and ASCC1-del-R2)

  • For confirming intact sequences in control cells, use primers ASCC1-del-D and ASCC1-WT-R

  • Optimize PCR conditions: Q5 Hot Start High-Fidelity 2x Master Mix, 98°C for 30s followed by 31 cycles of 98°C for 10s, 60°C for 30s, and 72°C for 15s, plus 7 min at 72°C

What assays are appropriate for studying ASCC1's effects on osteogenesis and adipogenesis?

A comprehensive assessment requires multiple complementary assays:

For osteogenesis:

• qPCR analysis of osteogenic markers:

  • Early markers: ALPL, RUNX2

  • Late markers: CTNNB1, osteocalcin

• Mineralization assays:

  • Quantification of incorporated calcium at day 25 of differentiation

  • Alizarin Red S staining for visualization of mineral deposits

• Signaling pathway analysis:

  • Assessment of TGF-β/SMAD signaling activation via pSMAD3/SMAD3 ratio

For adipogenesis:

• qPCR analysis of adipogenic markers:

  • PPARγ, FASN, and other adipocyte-specific genes at day 17 of differentiation

• Lipid formation assays:

  • Oil Red O staining of differentiated cells

  • Photometric quantification of extracted lipid droplets

• Morphological assessment of adipocyte formation

What techniques can identify the molecular consequences of ASCC1 mutations?

To comprehensively characterize ASCC1 mutation consequences:

• Transcriptomic analysis:

  • RNA sequencing of patient-derived cells vs. controls

  • Focus on differential expression of genes related to bone development, muscle function, and neurogenesis

• Proteomic profiling:

  • Quantitative proteomics to confirm protein expression levels

  • Phosphoproteomic analysis to assess altered signaling pathways

• Functional assays:

  • TGF-β/SMAD signaling analysis using recombinant human TGF-β1 stimulation

  • Assessment of pSMAD3/SMAD3 expression ratio

  • Analysis of RUNX2 and SERPINF1 expression through qPCR and Western blotting

How should researchers interpret bone histomorphometry data in ASCC1-deficient models?

When analyzing bone histomorphometry data from ASCC1-deficient models, researchers should:

• Assess these key parameters with quantitative metrics:

  Parameter	Expected Finding in ASCC1 Deficiency	Significance
  Trabecular bone volume	Decreased	Indicates reduced bone mass
  Bone remodeling activity	Decreased	Shows impaired bone turnover
  Collagen organization	Disordered	Reflects abnormal bone matrix
  Bone marrow adiposity	Increased	Demonstrates MSC fate shift
  Osteocyte morphology	Irregular shape and position	Indicates immature woven bone

• Use polarized light microscopy to specifically evaluate collagen fibril organization, looking for the accumulation of immature woven bone with irregularly shaped and positioned osteocyte lacunae

• Compare findings against age-matched controls, as bone parameters vary significantly with developmental stage

• Correlate histomorphometric findings with molecular markers (RUNX2, PPARγ) to establish mechanistic connections

How can researchers distinguish ASCC1-specific effects from generalized cellular stress responses?

To differentiate ASCC1-specific effects from general stress responses:

• Perform parallel experiments with knockdown of functionally unrelated genes to establish a baseline stress response

• Design rescue experiments:

  • Re-express wild-type ASCC1 in deficient cells

  • Create domain-specific mutants (KH domain vs. PDE domain)

  • Analyze which phenotypes are rescued by each construct

• Use time-course experiments to differentiate immediate (likely specific) from delayed (potentially compensatory) responses

• Employ pathway-specific inhibitors alongside ASCC1 manipulation to identify independent vs. convergent effects

What bioinformatics approaches best analyze ASCC1 mutations in cancer datasets?

For analyzing ASCC1 in cancer contexts:

• Survival analysis correlating ASCC1 expression with patient outcomes:

  • Use Kaplan-Meier curves with log-rank tests

  • Apply Cox proportional hazards models for multivariable adjustment

• Mutation signature analysis:

  • Focus on correlations with Signatures 29 and 3 mutations as identified in TCGA data

  • Evaluate associations with genetic instability markers

• Evolutionary action analysis:

  • Apply quantitative evolutionary analyses to assess functional impact of ASCC1 variants

  • Map cancer-associated mutations onto crystal structure to predict functional consequences

• Integration with molecular subtypes:

  • Correlate ASCC1 alterations with established cancer molecular subtypes

  • Identify co-occurring genomic alterations that may interact with ASCC1

What criteria define ASCC1-related SMABF2 and differentiate it from other neuromuscular disorders?

SMABF2 (spinal muscular atrophy with congenital bone fractures-2) presents with a distinctive clinical profile:

Definitive diagnosis requires identification of bi-allelic pathogenic variants in ASCC1, with carrier testing confirming recessive inheritance .

How might understanding ASCC1's structural biology inform therapeutic development?

The detailed structural understanding of ASCC1 reveals multiple potential therapeutic targets:

• RNA binding interfaces:

  • The novel Helix-Clasp-Helix (HCH) motif could be targeted by small molecules to modulate ASCC1-RNA interactions

  • Specific targeting of CGCG binding pocket might allow selective intervention

• PDE catalytic site:

  • The V-shaped nucleotide binding channel with two HXT motifs offers a druggable pocket

  • The atypical histidine rotamer in the second HXT motif creates a unique binding environment for selective compounds

• Regulatory elements:

  • The flexible ActSite-loop that coordinates with catalytic residues could be stabilized in active or inactive conformations

  • The arginine-rich domain linker offers another regulatory target

• Signaling pathway modulation:

  • Since ASCC1 deficiency inhibits TGF-β/SMAD signaling, compounds that enhance this pathway might compensate

  • RUNX2 activators could potentially overcome the osteoblastogenesis defects

What is the evidence for ASCC1's role in cancer progression and potential as a therapeutic target?

ASCC1's emerging role in cancer is supported by several lines of evidence:

• Prognostic significance:

  • TCGA data analysis shows ASCC1 RNA overexpression in certain tumors correlates with poor survival

  • This suggests potential as a prognostic biomarker

• Genetic instability connections:

  • ASCC1 overexpression correlates with Signatures 29 and 3 mutations

  • Association with genetic instability markers suggests involvement in DNA damage responses or genomic maintenance

• Mechanistic insights:

  • ASCC1 acts with ASCC-ALKBH3 complex in alkylation damage responses

  • This DNA damage response function may be exploited by cancer cells

• Structural vulnerabilities:

  • Crystal structures reveal potential binding pockets that could be targeted

  • The unique KH-PDE domain arrangement offers cancer-specific targeting opportunities

• Therapeutic implications:

  • Structure-based inhibitors targeting ASCC1 could be developed

  • Understanding of ASCC1's roles in transactivation and alkylation damage responses provides rational design strategies

What are the most promising approaches for studying ASCC1 in vivo?

Future in vivo research on ASCC1 should focus on:

• Conditional knockout mouse models:

  • Tissue-specific deletion in bone, muscle, and neural tissues to dissect organ-specific functions

  • Temporal control to distinguish developmental vs. maintenance roles

• Zebrafish models:

  • Building on existing zebrafish work showing that ASCC1 knockdown disrupts α-motoneuron outgrowth

  • Expand to study bone formation and fracture healing processes

• Patient-derived iPSCs:

  • Differentiation into relevant lineages (osteoblasts, neurons, muscle)

  • CRISPR correction of mutations to establish isogenic controls

• In vivo RNA binding studies:

  • CLIP-seq in relevant tissues to identify physiological RNA targets

  • Integration with transcriptome-wide structure mapping

How might ASCC1's evolutionary conservation inform research across model organisms?

ASCC1's high evolutionary conservation provides valuable research opportunities:

• Comparative structural analysis:

  • Human and Alvinella pompejana ASCC1 structures show conservation over 500 million years

  • The Ap ASCC1 structure overlays well with Hs ASCC1 (RMSD of 0.89 Å), supporting use as a model

• Cross-species functional studies:

  • Complementation experiments in various model organisms

  • Identification of species-specific vs. conserved functions

• Domain evolution analysis:

  • Separate study of KH and PDE domains across species

  • Understanding how the unique domain combination evolved

• Differential expression patterns:

  • Compare tissue-specific expression across evolutionary diverse organisms

  • Identify core vs. specialized functions

What technological advances are needed to fully characterize ASCC1's molecular functions?

Advancing ASCC1 research requires several technological developments:

• Advanced structural biology methods:

  • Cryo-EM studies of ASCC1 in complex with RNA and protein partners

  • Time-resolved crystallography to capture conformational changes during catalysis

• Improved RNA-protein interaction assays:

  • High-throughput methods to comprehensively map RNA binding specificities

  • Single-molecule techniques to observe real-time binding dynamics

• Tissue-specific proteomics:

  • More sensitive methods to detect low-abundance ASCC1 interactors

  • Proximity labeling approaches to identify transient interactions

• Advanced disease models:

  • Organoid systems to better model development and disease

  • Patient-derived xenografts for cancer studies

• Computational approaches:

  • Improved algorithms for predicting functional impacts of variants

  • Integration of evolutionary, structural, and functional data