Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum.

The recent development of CRISPR-Cas technology holds promise to correct gene-level defects for genetic diseases. The key element of the CRISPR-Cas system is the Cas protein, a nuclease that can edit the gene of interest assisted by guide RNA. However, these Cas proteins suffer from inherent limitations such as large size, low cleavage efficiency, and off-target effects, hindering their widespread application as a gene editing tool. Therefore, there is a need to identify novel Cas proteins with improved editing properties, for which it is necessary to understand the underlying features governing the Cas families. In this study, we aim to elucidate the unique protein features associated with Cas9 and Cas12 families and identify the features distinguishing each family from non-Cas proteins. Here, we built Random Forest (RF) binary classifiers to distinguish Cas12 and Cas9 proteins from non-Cas proteins, respectively, using the complete protein feature spectrum (13,494 features) encoding various physiochemical, topological, constitutional, and coevolutionary information on Cas proteins. Furthermore, we built multiclass RF classifiers differentiating Cas9, Cas12, and non-Cas proteins. All the models were evaluated rigorously on the test and independent data sets. The Cas12 and Cas9 binary models achieved a high overall accuracy of 92% and 95% on their respective independent data sets, while the multiclass classifier achieved an F1 score of close to 0.98. We observed that Quasi-Sequence-Order (QSO) descriptors like Schneider.lag and Composition descriptors like charge, volume, and polarizability are predominant in the Cas12 family. Conversely Amino Acid Composition descriptors, especially Tripeptide Composition (TPC), predominate the Cas9 family. Four of the top 10 descriptors identified in Cas9 classification are tripeptides PWN, PYY, HHA, and DHI, which are seen to be conserved across all Cas9 proteins and located within different catalytically important domains of the Streptococcus pyogenes Cas9 (SpCas9) structure. Among these, DHI and HHA are well-known to be involved in the DNA cleavage activity of the SpCas9 protein. Mutation studies have highlighted the significance of the PWN tripeptide in PAM recognition and DNA cleavage activity of SpCas9, while Y450 from the PYY tripeptide plays a crucial role in reducing off-target effects and improving the specificity in SpCas9. Leveraging our machine learning (ML) pipeline, we identified numerous Cas9 and Cas12 family-specific features. These features offer valuable insights for future experimental and computational studies aiming at designing Cas systems with enhanced gene-editing properties. These features suggest plausible structural modifications that can effectively guide the development of Cas proteins with improved editing capabilities.

A Role for Adipocytes and Adipose Stem Cells in the Breast Tumor Microenvironment and Regenerative Medicine.

Obesity rates are climbing, representing a confounding and contributing factor to many disease states, including cancer. With respect to breast cancer, obesity plays a prominent role in the etiology of this disease, with certain subtypes such as triple-negative breast cancer having a strong correlation between obesity and poor outcomes. Therefore, it is critical to examine the obesity-related alterations to the normal stroma and the tumor microenvironment (TME). Adipocytes and adipose stem cells (ASCs) are major components of breast tissue stroma that have essential functions in both physiological and pathological states, including energy storage and metabolic homeostasis, physical support of breast epithelial cells, and directing inflammatory and wound healing responses through secreted factors. However, these processes can become dysregulated in both metabolic disorders, such as obesity and also in the context of breast cancer. Given the well-established obesity-neoplasia axis, it is critical to understand how interactions between different cell types in the tumor microenvironment, including adipocytes and ASCs, govern carcinogenesis, tumorigenesis, and ultimately metastasis. ASCs and adipocytes have multifactorial roles in cancer progression; however, due to the plastic nature of these cells, they also have a role in regenerative medicine, making them promising tools for tissue engineering. At the physiological level, the interactions between obesity and breast cancer have been examined; here, we will delineate the mechanisms that regulate ASCs and adipocytes in these different contexts through interactions between cancer cells, immune cells, and other cell types present in the tumor microenvironment. We will define the current state of understanding of how adipocytes and ASCs contribute to tumor progression through their role in the tumor microenvironment and how this is altered in the context of obesity. We will also introduce recent developments in utilizing adipocytes and ASCs in novel approaches to breast reconstruction and regenerative medicine.

Adipose Stem Cells in Regenerative Medicine: Looking Forward.

Over the last decade, stem cell-based regenerative medicine has progressed to clinical testing and therapeutic applications. The applications range from infusions of autologous and allogeneic stem cells to stem cell-derived products. Adult stem cells from adipose tissue (ASCs) show significant promise in treating autoimmune and neurodegenerative diseases, vascular and metabolic diseases, bone and cartilage regeneration and wound defects. The regenerative capabilities of ASCs in vivo are primarily orchestrated by their secretome of paracrine factors and cell-matrix interactions. More recent developments are focused on creating more complex structures such as 3D organoids, tissue elements and eventually fully functional tissues and organs to replace or repair diseased or damaged tissues. The current and future applications for ASCs in regenerative medicine are discussed here.

Numerical investigation of the effect of bone cement porosity on osteoporotic femoral augmentation.

Femoroplasty is the injection of bone cement into the proximal femur, enhances the bone load capacity, and is typically applied to osteoporotic femora. To minimize the required injected volume of bone cement and maximize the load capacity enhancement, an optimization problem must be solved, where the modulus of elasticity of the augmented bone is a key element. This paper, through the numerical investigation of a fall on the greater trochanter of an osteoporotic femur, compares different ways to calculate this modulus and introduces an approach, based on the concept of bone cement porosity, which provides results statistically similar to those obtained with other considerations. Based on this approach, the present paper quantifies the correlation between degree of osteoporosis and optimum volume of bone cement. It concludes with an exhaustive search that reveals the effect of the bone cement porosity on the optimum volume of PMMA, for various combinations of the frontal and transverse angles of the fall on the greater trochanter.

A new evolutionary optimization method for osteoporotic bone augmentation.

Bone augmentation is a preventative osteoporosis intervention, comprising the injection of bone cement into an osteoporotic bone. As injection of excessive amounts of bone cement may result into thermal necrosis of bone tissue or even embolism, the minimum cement volume required to achieve a predefined level of augmentation must be sought. To this end, the present paper introduces a new evolutionary optimization method, applicable to any osteoporotic bone. The method was numerically evaluated through a typical case of femoral augmentation and compared to another powerful optimization method. The results demonstrate the efficiency and low computational cost of the proposed method.