AI Reveals The Reason Cholera Is So Deadly

A group of researchers at the University of Nottingham (UK) have used a computational framework to determine how the cholera-causing bacteria are so deadly. These microbes are evolving to make the disease more severe and difficult to clinically treat, but, why this happens is not understood. The scientists used a combination of protein and genetic analysis to reveal new disease mechanisms at play, setting the scene for the future of bacterial research.

 Cholera: Nothing New, But Changing At A Worrying Rate

According to WHO, there are an estimated 1.3−4.0 million cases of cholera every year, giving rise to a shocking 21,000−143,000 deaths globally.

Cholera is a diarrheal disease that can lead to death quickly if left untreated, sometimes even within hours of onset.

Those with severe bouts (making up ~1 in 5 cases) need immediate treatment with IV fluids, to replace any water lost, and antibiotics, to fight the infection. It is a particularly problematic disease in impoverished communities, where there is limited access to clean water and proper sanitation.

While cholera is no new phenomenon, the bacteria behind the disease, called vibrio cholerae, are evolving, causing infections that are more dangerous and difficult to manage, especially in more poorly-resourced areas.

Understanding Cholera Is Easier Said Than Done

Presently, it is not clear why some cases are much more severe than others. Understanding the disease is further complicated by the dynamic choleric genomic landscape.

Unlocking the genomic secrets behind disease severity and cholera’s evolution holds great promise for developing targeted therapies to save lives. By understanding the genetic factors driving cholera, researchers can create more effective treatment strategies for those affected.

Genomic analysis of cholera-causing bacteria is, nonetheless, exceptionally challenging. For example, there is a large amount of genetic variability between bacteria (even if they are of the same strain), and bacterial genomes are extraordinarily diverse. This makes datasets complex and time-consuming to understand, hindering any progress in understanding the genetic reasoning behind evolutionary shifts or disease severity.

On top of this, there are many different variables that can drive bacterial evolution; it’s still uncertain whether gene regulation (how genes are switched on/off), protein folding (the way proteins take their shape) or metabolic networks (biochemical reactions that provide energy or process biological signals) orchestrate evolutionary shifts.

These confounding aspects cloud scientists’ understanding of cholera-causing bacteria.

AI Bring The Landscape Of Bacterial Evolution Into Sharp Focus

A study from researchers at the University of Nottingham is however offering a new perspective.

The group studied the stool samples of cholera patients, collected from 6 distinct regions across Bangladesh. To analyse these samples, a combination of techniques was used, including machine learning, whole-genome sequencing , genome-scale metabolic modelling  and 3D structural analysis.

This combinatorial approach let the researchers uncover relationships between the genetic makeup of the bacteria, the way in which they evolve or spread overtime, and the severity of disease that they cause.

Specifically, the integration of machine learning let the researchers look at the entire genome of the bacteria and compare this to clinical characteristics of the disease. In this paradigm, the strength of the correlation between a genetic and observable trait (i.e., disease severity) indicates a strong link between the two.

Implications For The Future Of Cholera Research

This study sets a new precedent for cholera research, and, indeed, bacterial research on the whole.

AI is able to significantly boost the power of genomic analysis, and, when combined with an artillery of other experimental techniques, unlocks the door to finally understanding how cholera can cause such fatal disease and how there may be more severe cases in future.

As such, AI could be used to reveal new therapeutic opportunities and treatment strategies when trying to compete with such deadly disease-causing bacteria.

Looking forward, scientists may use this pivotal study as a beacon of light for the field of infectious disease.