Naturally occurring cell death is essential to the development of the mammalian nervous system. Although the importance of developmental cell death has been appreciated for decades, there is no comprehensive account of cell death across brain areas in the mouse. Moreover, several regional sex differences in cell death have been described for the ventral forebrain and hypothalamus, but it is not known how widespread the phenomenon is. We used immunohistochemical detection of activated caspase-3 to identify dying cells in the brains of male and female mice from postnatal day (P) 1 to P11. Cell death density, total number of dying cells, and regional volume were determined in 16 regions of the hypothalamus and ventral forebrain (the anterior hypothalamus, arcuate nucleus, anteroventral periventricular nucleus, medial preoptic nucleus, paraventricular nucleus, suprachiasmatic nucleus, and ventromedial nucleus of the hypothalamus; the basolateral, central, and medial amygdala; the lateral and principal nuclei of the bed nuclei of the stria terminalis; the caudate-putamen; the globus pallidus; the lateral septum; and the islands of Calleja). All regions showed a significant effect of age on cell death. The timing of peak cell death varied between P1 to P7, and the average rate of cell death varied tenfold among regions. Several significant sex differences in cell death and/or regional volume were detected. These data address large gaps in the developmental literature and suggest interesting region-specific differences in the prevalence and timing of cell death in the hypothalamus and ventral forebrain.

Aims: Heart valve disease attributed to serotonin (5HT) has been observed with 5HT-secreting carcinoid tumors and in association with medications, such as the diet drug, Dexfenfluoramine, a serotonin transporter (SLC6A4) inhibitor and 5HT receptor (HTR) 2B agonist. HTR2B signaling upregulates TGFβ-1 resulting in increased production of extracellular matrix proteins. SLC6A4 internalizes 5HT, limiting HTR signaling. Selective 5HT reuptake inhibitors (SSRI), widely used antidepressants, target SLC6A4, thus enhancing HTR signaling. However, 5HT and SLC6A4 mechanisms have not been previously associated with degenerative mitral regurgitation (MR). The present studies investigated the hypothesis that both dysregulation of SLC6A4 and inhibition of SLC6A4 contribute to the pathophysiology of MR. Methods and Results:Here we report SLC6A4 related studies of 225 patients with MR requiring surgery. A multivariate analysis showed that SSRI use in MR patients was associated with surgery at a younger age, indicating more rapidly progressive MR (p=0.0183); this was confirmed in a national dataset (p<0.001). Aspirin use by MR patients was associated with surgery at an older age (p=0.0055). Quantitative reverse transcriptase PCR of MR leaflet RNA from 44 patients, and 20 normal mitral leaflets from heart transplant recipients, demonstrated down regulation in MR of both SLC6A4 and vesicular monoamine transporter-2 (SLC18A2), that packages 5HT (p<0.001). Human mitral valve interstitial cells cultivated with Fluoxetine, a SSRI, demonstrated down regulation of SLC6A4 and upregulation of HTR2B, compared to untreated, in cells from both normal and MR leaflets. Platelet 5HT studies in healthy subjects without heart disease used ADP-induced activation to model MR-associated activation. Fluoxetine significantly increased platelet activation and plasma 5HT levels, while Aspirin inhibited ADP platelet activation.Conclusions: Down regulation and inhibition of SLC6A4 influences MR through enhanced HTR signaling. SSRI may further influence MR through inhibition and down regulation of SLC6A4, upregulation of HTR2B, and increased platelet release of 5HT. Translational PerspectiveDegenerative mitral valve regurgitation (MR) affects millions, and there is no medical therapy for this disease. MR becomes progressively worse, and for severe MR, the only option is cardiac surgery. Serotonin (5HT) is best known as a neurotransmitter. However, 5HT secreting carcinoid tumors cause a cardiac valve disorder in many cases, and 5HT related medications, such as the diet drug Fenfluoramine, have been associated with the development of cardiac valve disease. The present paper presents evidence that diminished serotonin transporter (SLC6A4) expression and inhibition, lead to increased 5HT receptor signaling, contributing to the progression of MR.

The computational methods used for engineering antibodies for clinical development have undergone a transformation from three-dimensional structure-guided approaches to artificial-intelligence- and machine-learning-based approaches that leverage the large sequence data space of hundreds of millions of antibodies generated by next-generation sequencing (NGS) studies. Building on the wealth of available sequence data, we implemented a computational shuffling approach to antibody components, using the complementarity-determining region (CDR) and the framework region (FWR) to optimize an antibody for improved affinity and developability. This approach uses a set of rules to suitably combine the CDRs and FWRs derived from naturally occurring antibody sequences to engineer an antibody with high affinity and specificity. To illustrate this approach, we selected a representative SARS-CoV-2-neutralizing antibody, H4, which was identified and isolated previously based on the predominant germlines that were employed in a human host to target the SARS-CoV-2-human ACE2 receptor interaction. Compared to screening vast CDR libraries for affinity enhancements, our approach identified fewer than 100 antibody framework–CDR combinations, from which we screened and selected an antibody (CB79) that showed a reduced dissociation rate and improved affinity against the SARS-CoV-2 spike protein (7-fold) when compared to H4. The improved affinity also translated into improved neutralization (>75-fold improvement) of SARS-CoV-2. Our rapid and robust approach for optimizing antibodies from parts without the need for tedious structure-guided CDR optimization will have broad utility for biotechnological applications.

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The application of Machine Learning (ML) tools to engineer novel antibodies having predictable functional properties is gaining prominence. Herein, we present a platform that employs an ML-guided optimization of the complementarity-determining region (CDR) together with a CDR framework (FR) shuffling method to engineer affinity-enhanced and clinically developable monoclonal antibodies (mAbs) from a limited experimental screen space (order of 10^2 designs) using only two experimental iterations. Although high-complexity deep learning models like graph neural networks (GNNs) and large language models (LLMs) have shown success in protein folding with large dataset sizes, the small and biased nature of the publicly available antibody-antigen interaction datasets is not sufficient to capture the diversity of mutations virtually screened using these models in an affinity enhancement campaign. To address this key gap, we introduced inductive biases learned from extensive domain knowledge of protein-protein interactions through feature engineering and selected model hyperparameters to reduce the overfitting of the limited interaction datasets. Notably, we show that this platform performs better than GNNs and LLMs on an in-house validation dataset that is enriched in diverse CDR mutations that go beyond alanine-scanning. To illustrate the broad applicability of this platform, we successfully solved a challenging problem of redesigning two different anti-SARS-COV-2 mAbs to enhance affinity (up to 2 orders of magnitude) and neutralizing potency against the dynamically evolving SARS-COV-2 Omicron variants.